With frequent market volatility and the need to increase the chances of meeting long and short term financial goals, financial advice services for individual as well as institutional investors have become very popular in the financial sector. The value of financial advice to individual as well as institutional investors is extensively studied in the literature (Marsden et al., 2011; Kramer, 2012; Collins, 2012; Finke, 2013; Pagliaro and Utkus, 2019).
The value of financial advice may not only come in the form of determining the investor’s financial risk tolerance and loss aversion; selecting appropriate financial products; constructing personalized and diversified financial portfolios to suit the individual’s short and long term goals; improving tax efficiency of the savings; but also comes from behavioral coaching during adversarial financial conditions (both, the investor’s personal financial situations and setbacks, as well as the market fluctuations). Though the robo-advisors have become popular in the recent years for the cost benefits and their wide and easy accessibility, even the investor working with a robo-advisor values the possibility to reach out and interact with humans (Rossi and Utkus, 2020a; Madamba et al., 2020).
In the present work, with the help of machine learning (ML) techniques, we study advisor-investor interactions in order to predict the advised investors’ needs and use these predictions to help advisors proactively coach their clients during adverse market conditions. In particular, we use natural language processing (NLP) techniques such as topic modeling and words embedding to extract behavioral insights from financial advisors’ summary notes.
Below, we review the existing literature on data-driven investors’ modeling as well as interactions between financial advisors and their clients.
1.1. Previous Work
Most ML-focused research in Finance areas focuses on market and investment performance; far less on actual investor behavior. In the case of the latter, there are comprehensive studies, utilizing more traditional statistical techniques that explore savings, spending and investment behavior among institutional, retail and advised investors (Vanguard, 2020, 2021). Though investors’ behavior as well as interaction between financial advisors and investors has been investigated from traditional finance and behavioral finance (Barberis and Thaler, 2005; Bennyhoff et al., 2018) areas, the literature on purely data-driven investigations of these topics and, especially, using machine learning, is sparse.
There are, however, indeed a few important research works that have laid foundations to this area: in (Silva et al., 2019), a relatively large and high-frequency trading dataset of 13,000 investors of a large bank in Brazil between 2016 and 2018 was analyzed to find evidence of mean-reverting investment strategies (investors decide their investment strategies using recent past price changes), and a correlation between gender as well as academic background with mean-reverting strategy.
In (Obaid and Pukthuanthong, 2021)
, computer vision techniques were used to extract information from large sample of photos published in the press to create a daily investor sentiment index. On the other hand, a similar measure was developed based on the valence of songs that individuals listen to, which in turn captures seasonal mood swings, and was shown to be associated with a systematic pattern of mispricing correction (especially, for stocks with greater limits to arbitrage)(Fernandez-Perez et al., 2020).
In (Rossi and Utkus, 2020b)
, account balance and trading dataset of 50,000 investors signed up with the personal advisory services at Vanguard was investigated using boosted decision trees and concluded that the investors that benefited the most from the advisor service were the clients with little investment experience, and those who had high cash-holdings and high trading volume pre-adoption.
In (Thompson et al., 2021), the authors analyzed a one year data of 23,000 investors, who were actively working with financial advisors, with the dataset including investors’ demographic, recency, frequency and monetary related variables. The authors used unsupervised clustering technique to identify groups of investors that behaved similarly, and then showed that only demographic information of the investors, such as gender, residence region, and marital status, could not explain investors’ behaviours, whereas the variables for trade and transaction frequency and volume were most informative.
As for specifically investigating the advisor-investor interaction, (Guo et al., 2015) studied tweets between financial advisors and investors to establish an association between investor attention and market fluctuations. Similarly, (Zhang and Wang, 2015) using Baidu index (a search engine index corresponding to search-volume and frequency of keywords and phrases) as a proxy to investors’ attention, a correlation between individual investors’ attention and ChiNext stock market performance. In (Tauni et al., 2020), personality similarity between an advisor and investor, measured using an online survey questionnaire, was shown to be related to investor stock trading performance. In particular, it was shown that the investor-advisor similarity in terms of openness, extraversion, conscientiousness and agreeableness was positively related to the corresponding investor’s stock trading performance, whereas the similarity in neuroticism negatively affected trading performance (see also, (Monti et al., 2014)).
1.2. Our Contribution
In the present work, we explore the interaction between the financial advisor and investors by analyzing the unique data of advisors’ summary notes prepared after every meeting with the investor.
The notes may have free format and contain both manually written notes as well as text selected from pre-filled drop-down menus in the Customer Relationship Management (CRM) system. From the machine learning point of view, each of the advisor notes is an individual document with textual data.
Machine learning techniques, particularly, NLP, have been applied with great effect in a variety of industries. Within financial services, NLP techniques have been used to perform sentiment analysis, topic modeling, relationship extraction, automatic summarization, etc. for the textual data such as news articles, earning statements, manager presentations and acquisition announcements and social media posts(Li et al., 2014; Schumaker et al., 2012; Mishev et al., 2020; Jaggi et al., 2021; Sohangir et al., 2018). However, to the best of our knowledge, the present work is the first ever work to analyze advisor-investors interaction as well as to investigate behavioral aspects from the data to identify investors in need of proactive financial and behavioral coaching.
In this paper, we begin by applying the topic modeling technique, an NLP technique that summarizes large collection of textual information into more meaningful data elements, to the financial advisor notes data and demonstrate how this methodology can (1) help better understand the nature of advisor/client discussions and (2) convert advisor notes into a data element that can be utilized in more sophisticated behavioral models.
2. Data Description
In this section, we provide details of various data sources analyzed in the present work.
2.1. Advisor Notes
Financial advisors conduct both regularly scheduled and ad hoc meetings with their clients. After each meeting, advisors document key aspects of the conversation in a CRM system within one to three days of the interaction. Overall, our sampled data for this paper includes about 1.5M notes from 2018 to 2020. Those notes were taken by more than 1,000 advisors for about 150,000 investors. Figure 1 shows the monthly note total and number of notes per advisor. Typically, an advisor creates about 25 to 45 notes in a month.
While basic guidelines for documentation are outlined, advisors utilize a method or style that best works for them. Figure 2 shows the histogram of the average length of notes created by advisors from year 2018 to 2020. This histogram indicates half of the advisors typically keep short and brief notes (average length is less than 250 characters), and a sizeable group of advisors keep thorough and comprehensive notes.
Some notes are very detailed while others are simply a series of bullet points. We show a portion of the advisor note below for illustration,
Allison and Bob. Discussed MV. They don’t seem too worried. I reassured them that they are only 35% stocks AA, and will check on regular basis. They are now in their new assisted living facility. They like it.
Some stylistic factors create a challenge for NLP. As above, commonly used financial phrases by advisors such as “asset allocation” and “market volatility” may be abbreviated as “AA” and “MV”. Some advisors prefer to keep notes with bullet points and the presence of bullets and other erroneous characters should also be taken care of before most of the NLP downstream tasks, such as topic modeling.
In general, text data preparation involves several steps: (1) using regular expression to remove non-text characters or irreverent text that can be identified through search pattern; (2) tokenize words and remove stop words; (3) creation of bigrams and trigrams; and, (4) lemmatization. For brevity, we skip the details about the above steps, but illustrate the data preparation and preprocessing procedures with the following examples, as illustrated in Table 1.
The first step is to tokenize the words and remove any stop words like “and”, “in”, or “it”;
Second, create bigrams and trigrams to capture the correct meaning, for example, the bigram “regular-basis” and trigram “assisted-living-facility” indicate the appropriate meaning of the words combined;
Lemmatization is used to map variations of the word back to its simplest form.
Once all three steps are completed, the words can be kept as a dictionary which is ready for the modeling tasks that follow.
2.2. Transaction Data
Transaction data are a collection of trading records from investors, and the data reflect not only the trading behavior of investors but the outlook for the financial markets. Our transaction data include three basic elements: financial instruments (stocks, exchange traded funds, etc.), transaction types (buy, sell, etc.), and transacted amounts. The data is comprised from six-month trade and transaction details for about 150,000 investors. This data was processed, and features were derived as part of the inputs for investor behavior prediction model described in Section 4.2. The derived features are designed to capture the investors’ trading/transaction behavior, and they can be summarized into three types as shown in Table 2.
|Tokenization, stopword removal||allison, bob, discussed, MV, they, dont, seem, worried, reassured, them, they, only, stocks, AA, will, check, regular, basis, they, their, new, assisted, living, facility, they, like|
|Creation of bigrams and trigrams||regular-basis, assisted-living-facility|
|Lemmatization||discuss, worry, reassure, assist|
|Recency||Capture investors’ weekly trading patterns, number of trades clustered in recent days, investors’ trading habits (regular or sporadic)|
|Frequency||Total number in different types of trades, total number of trades in different accounts, total number of trades for the same financial instruments|
|Monetary||Total amount in different types of trades, total amount of trades in different accounts, total amount of trades for the same financial instruments|
2.3. Market Data
In the finance literature, the volatility of any security, fund or market trading price series is defined as the amount of variation of the price series over time and is usually measured by the standard deviation of logarithmic return. The volatility can be further specified as past, current or future volatility depending on the context of the volatility computation. There are various volatility indices available from different data vendors and institutions. We use the Chicago Board Options Exchange (CBOE) Volatility Index (VIX)(Exchange, 2009). In the present work, the weekly average of VIX index was used to re-weight the significance of transactions. For instance, we put more emphasis on transactions that occurred during high VIX index when creating features from the transaction data.
In this section, we describe the methodology for both topic modeling and supervised classification used in the present work.
3.1. Topic Modeling
Latent Dirichlet Allocation
Discovering topics out of a collection of text documents is a traditional problem in machine learning that is extensively studied yet keeps posing different challenges. These include the variety of topics, the choice of topic modeling algorithm, the number of topics and tuning parameters used in the model. The topic modeling process involves both data preparation (described in Section 2.1) and model generation.
In this work, we used one of the most well-studied and popular model, called Latent Dirichlet Allocation (LDA)(Blei et al., 2003), to identify topics from advisor notes. Here, with the ‘bag-of-words’ encoding of each document, and a pre-determined number of topics, , the algorithm starting with random assignment at first keeps computing and updating probability of a word being in one of the topics till it converges at a stopping criterion.
Metric for Topic Modeling
To find the optimal value for , one scans over a range of integer values and choses when a chosen metric is optimized. In the present work, we have used coherent score () (Röder et al., 2015), which is computed over sliding windows, as the metric to evaluate the quality of topic modeling. We used the sliding window size as default of 110 words.
To capture the domain specific language used by the financial advisors as well as achieving semantic similarity among words in the available corpus of text, we employed the Word2Vec embedding by starting it from scratch on our data. The main principle behind Word2Vec algorithm is that the meaning of a word can be inferred from its surrounding words. A neural-network-based algorithm is employed to learn word associations from a large text corpus and present each word as a vector. The word vectors, widely known as word embeddings, carry semantic information about words. Once trained, this model can detect synonyms for a user given word based on the training data. The level of semantic similarity between words can be measured by simple mathematical functions such as the cosine similarity between vectors. In the present work, we used the Word2Vec model that was trained on all the available notes.
The common algorithms used for training a Word2Vec model are Continuous Bag of words (CBOW) and Skip-gram (Mikolov et al., 2013)
. The Skip-gram is an unsupervised learning technique used to find the most relevant words for a given word. This algorithm predicts the context words for the given target word. Conversely, the CBOW algorithm predicts the probability of a target word while given context words.
We first preprocessed the advisor notes with the four steps described in Section 2.1 and utilized Skip-gram algorithm implemented in Gensim Python library (Rehurek and Sojka, 2010) to train the Word2Vec model and to obtain the word embeddings. Several parameters we chose during the Word2vec model training are summarized below. The dimension of the word vector is set to 100, window size to 2, and minimum word frequency to 20 to filter out the infrequent words. We also leveraged the negative sampling approach (Goldberg and Levy, 2014) to speed up the training process and the number of negative samples was set to 20.
This Word2Vec model and the corresponding word embeddings are trained from our advisor notes corpus from scratch. Therefore, common financial phrases and their abbreviations used by our advisors are included, and the semantic information are naturally captured in the embeddings. For example, the embedding vectors of market volatility and MV are close to each other when measured by the cosine similarity. The advantages of using a Word2Vec model trained from our unique corpus are (1) abbreviations can be kept for downstream tasks and that would reduce the amount of preprocessing work, (2) spelling errors, synonyms, common usage problems are automatically handled since their semantic information is more relevant, (3) the Word2Vec helps to extract targeted information (defined by keywords) from the notes as described in Section 4.2.
3.3. Classification Models
We employed different binary classification algorithms to train and predict the probability of an investor requiring intervention and behavioral coaching by their advisor: Logistic regression (to obtain the base line) and Gradient Boosting.
Logistic regression is one of the most studied and widely applied models for binary classification tasks that uses a logistic function of linear combination of input variables to model a binary target variable. We use this model to obtain base-line results to be matched or out-performed by more complex machine learning models.
Decision Tree (DT) is one of the most powerful non-linear and yet interpretable machine learning algorithms that attempts to identify the decision process for the given regression or classification task from the data. Here, the depth (number of levels to cascade the tree to) of the tree is a hyperparameter and needs to be tuned to trade-off between bias and variance. However, DT is prone to overfitting, and sometimes its extension called Gradient Boosting Trees is preferred over DT.
Gradient Boosting (GB) Trees is an ensemble learning method based on multiple “weak” learners, in this case, DTs: instead of constructing one DT, multiple DTs are constructed. Then, an aggregate of all these DTs is used to predict the final output. Here, the depth of each DT as well as the number of DTs both are some of the hyperparameters and need be tuned to improve learning. A GB evade the overfitting problem of an individual DT by ensembling multiple DTs, though the GB then is less interpretable. One can compute variable importance from GB that can provide interpretability up to certain extent.
Metrics for Classification
Since we have a binary classification task at hand, the metrics can be defined based on four basic quantities: True Positive (
) which is the number of data-points from class 0 that are correctly classified by the model; True Negative () which is the number of data-points from class 1 that are correctly classified by the model; False Negative () which is the number of data-points from class 0 that are incorrectly classified by the model; and, False Positive () which is the number of data-points from class 1 that are incorrectly classified by the model.
Accuracy. Accuracy is defined as
, i.e., the fraction of predictions which were predicted correctly by the model. For imbalanced class problems though, accuracy may yield misleading conclusions. For such problems, the F1 score and area under the receiver operating characteristic curve (AUC-ROC) are better metrics.
F1 score. The F1 score is defined as , which takes values between 0 and 1, where F1 equals 1 meaning a perfect classification. To take the highly imbalanced data, we used the weighted F1 score which weighs the contributions of each class proportional to the respective number of data-points.
AUC-ROC. The receiver operating characteristic (ROC) curve is a plot of the fraction of the true positive rate (TPR) vs the fraction of the false positive rate (FPR) and it yields the performance of the binary classifier as a function of the discrimination probability threshold. The area under the ROC curve (AUC-ROC) is interpreted the probability that a classifier will rank a randomly chosen data-point of class 0 higher than a randomly chosen data-point of class 1.
Cross-Validation and Hyperparameter Optimization
To identify the best hyperparameter point as well as to prevent the model from overfitting the data, we used cross validation for the RF and GB models. Here, the -fold cross validation method was used where the training set is spilt into smaller sets. The given model is then trained on of the folds and validated on the remaining fold. We used stratified 5-fold cross validation which ensured that all the folds the same percentage of samples of each target class.
The objective of this study was two-fold: First, determine the main themes of the advisor-investor interaction. Second, determine if/how these themes change over market conditions. In this section, we begin by peeking into the advisor notes and providing basic statistics of the data. Then, we obtain a high-level comprehension about the given textual data through topic modeling. With the insights derived from topic modeling, the second use case is to extract targeted information, augment with transaction data, and build a model for investor behavior prediction.
4.1. First Insights into Advisor Notes
We chose the notes from March 2019 and March 2020 because they represent two different financial market conditions, the former being fairly “typical” whereas during the latter particularly volatile due to the Covid-19 pandemic. The total number of notes in those two months was about 150,000.
The histogram of the length of notes shown in Figure 3 highlights this variation in note taking style. On average, a note contains less than 250 characters excluding all whitespaces. Hence, the length of a note is about the same as a long tweet (according to Twitter’s 280-character limit). While some advisors take detailed notes, most of them are keeping the notes short and brief.
To determine the optimal number of topics during the LDA modeling stage, we used the coherence score () to choose the number of topics, i.e. , the number of topics corresponding to the highest value of was considered as the optimal number of topics. The coherence scores for a range of number of topics are shown in Figure 4. For the notes from March 2019 and March 2020, clearly the optimal number of topics should be 20.
In Table 3, we list the 20 topics for 2019 notes. We also show the percentage of notes assigned to (or related to) each topic and the topic keywords associated with the topic. For instance, Topic Quarterly review for asset allocation and rebalance represents around 13.3% of notes defined by the keywords: “send”, “target”, “allocation”, “current”, “email”, “rebalance”, “complete”, “quarterly”, etc.
In addition to the raw topics from the LDA model, we also include the topic labels in Table 3. These topic labels were inferred from the keywords based on our best judgement, and understandably the topic label creation is a subjective process.
A quick scan of the topic labels shows some commonality in the individual topics, particularly to a subject matter expert. In this case, a consensus view among independent subject matter experts was conducted to further consolidate the topics based on the topic keywords and given labels, thus reducing the number of topics from 20 to 7. This process was performed for the modeling results for both periods and the consolidation is shown in Figure 5.
During both periods, Asset allocation review and discussions is the most common theme, representing 27% and 26% of the advisor notes, respectively. Given that the primary purpose of regularly scheduled meetings or advisor-investor interactions is to review investors’ financial portfolio, Asset allocation review and discussions is expected to be the major theme in both periods and those periods excluded from the topic modeling.
|13.3%||send, target, allocation, current, email, rebalance, complete, quarterly, imh, trade||Quarterly review for asset allocation and rebalance|
|7.4%||schedule, appointment, call, follow, question, email, appt, message, leave, advisor||Client communications and schedule appointments|
|6.5%||plan, discuss, service, update, step, consent, strategy, implement, discussion, accept||Discuss advisory methodology, possibly client onboarding|
|6.0%||account, manage, transfer, move, taxable, open, joint, fee, trade, process||Upgrade inherited account|
|5.9%||fund, cash, spend, invest, money, spending, balance, emergency, position, agree||Spending/saving/investing strategies|
|5.8%||stock, bond, fund, gain, total, recommend, portfolio, index, hold, sell||Investment/portfolio discussions|
|5.7%||tax, make, contribution, taxis, distribution, state, amount, taxable, conversion, speak||Distributions and taxation|
|5.6%||send, set, confirm, check, bank, form, acct, receive, add, request||Client administration|
|4.8%||asset, plan, allocation, flow, tolerance, risk, cash, relationship, step, build||Intro call-asset allocation|
|4.4%||year, work, live, daughter, son, travel, kid, trip, enjoy, family||Family/hobbies|
|4.4%||client, update, account, conversation, discussion, profile, set, result, video, service||Client administration|
|3.9%||call, speak, today, week, back, time, give, day, number, end||Client administration|
|3.7%||investment, manage, portfolio, advisor, advice, strategy, management, financial, interested||Retirement planning|
|3.6%||trust, planning, cost, estate, care, insurance, mention, life, pass, health||Financial plan|
|3.6%||market, portfolio, return, performance, explain, volatility, concern, risk, understand, time||Market discussion|
|3.4%||home, pay, sell, move, money, buy, purchase, put, year, mortgage||Personal/family/life events|
|3.3%||year, expense, income, month, cover, increase, pay, amount, cost, start||Inheritance|
|3.1%||retirement, plan, work, age, retire, goal, year, saving, time, benefit||Retirement planning|
|3.1%||review, discuss, change, show, spending, tool, future, dynamic, outlook, add||Account review|
|2.8%||talk, make, good, time, move, thing, feel, share, point, give||Set up new account|
However, there is a distinct shift in two themes as highlighted in Figure 5. The proportion of advisor notes containing the Financial planning theme increases from 10% to 18%, and the Market discussion from 4% to 18%. These significant shifts in topic themes clearly were driven by the financial market conditions. Further, the percentages for Family/hobbies are about the same for both periods potentially indicating the standard conversation starters as well as conversations related to personal situations and goals.
The findings through topic modeling and topic theme identification help us better understand the nature of the financial advisory interaction. Moreover, using advisor notes to study the qualitative value that an advisory service can provide also helps us evaluate the efficacy of behavioral coaching.
4.2. Investor Behavior Prediction
While performing topic modeling using advisor notes gave unique insights into and understanding about the financial advisory interaction, we further focused on specific topic(s) such as market discussion, financial planning, etc. and utilizing them to develop a model which predicts the behavior of the investor and the timing for the advisor to proactively intervene and provide behavioral coaching. In other words, we further used NLP to extract targeted information from advisor notes by building a model that predicts investor behavior, i.e., if the investor would move all asset into cash during a volatile financial market period.
Information Extraction using Word2Vec
Since the model we are after is supposed to predict investor behavior in a volatile market, one natural consideration for the model inputs would be around (1) if an investor has revealed their concern for a volatile market; (2) if an investor is particularly anxious about market downturn; and (3) if an investor is sensitive and/or fear about market fluctuation.
Such information can be identified and tagged within advisor notes using the aforementioned NLP technique. For example, the word “volatility” representing the topic about market volatility can be used to determine if market volatility was a topic in advisor notes. The process of identifying and tagging a given word in texts data can be more than just search verbatim.
One common approach is to convert all the tokenized words into their Word2Vec embedding, a vector representation carrying semantic relationship about words. Then the closeness between words in advisor notes and the word representing a topic can be measured by the cosine similarity between the corresponding vectors in the embedding. That is, when the similarity measure for a word and “volatility” is above a particular threshold, this word is considered close to “volatility” in the semantic sense, and we can apply a tag to this word, representing the topic has been located.
More concretely, we show the top ten words similar to “volatility” from our Word2Vec model in Table 4. Some of them are phrases that can be used in place of “volatility”, and some of them are misspelled (indicated by asterisk). If we chose 0.84 as the minimal level of similarity measure, those ten words can be identified and tagged if occurred in the advisor notes. The proper minimal level of similarity measure (or threshold) can vary from one topic to the other. However, we empirically set the threshold as 0.7 for all topics during the information extraction phase.
Once the words about a topic are identified and tagged, we performed feature engineering on those tags. Here we show a few feature engineering considerations for our prediction task. The features from advisor notes can be (1) how often does a tag appear in the notes, (2) is the occurrence of tag significantly higher when normalized by the number of notes for a given client, (3) does the tag occur more often recently, (4) are the underlying similarity measures of tags closer to one (hinting that words are fully matched with the topic)?
The topics considered for this case study fall into two categories: one is about market volatility (represented by keywords “market” and “volatility”), and the other is about investors’ peace of mind (represented by keywords “sensitive”, “concern”, “panic”, etc.).
The features created from advisor notes constitute one part of the inputs for our model predicting investor behavior. The rest of the inputs are the features derived from the transaction data. Those features include number of days making trades, weekly trade frequency weighted with VIX price (described in Section 2.3), the frequencies and amounts in different types of trades, week-to-week transaction pattern, etc.
The dataset we prepared for this case study includes features and labels for about 150,000 investors. For each investor, there are more than 100 features characterizing market volatility and peace of mind topics from advisor notes and about 50 features capturing trading behavior from transaction data.
Further, the label is a binary value indicating if the investor chose to move all assets into cash. Since only a small fraction of investors in our dataset move all assets to cash, the dataset is highly imbalanced, posing a challenge for predictive modeling. To mitigate this issue, we have used various metrics such as weighted F1 score and AUC-ROC as discussed in Section 3.3.
Below, we discuss the results and performance of the models described in Section 3.3. The averages of accuracy and weighted F1 scores for three classification models are shown in Table 5. Noticeably, the GB model outperforms the Logistic regression model in both metrics. In practice, the AUC-ROC is an alternative for imbalanced classification problem. We show the ROC curves for training and test data in Figure 6. Since we employed stratified 5-fold cross validation, Figure 6 is the result from just one representative fold.
We also examine the feature importance based on the modeling results. The features are ranked according to the absolute values of the coefficients from Logistic regression model. In Figure 7, we show the percent of features derived from advisor notes when consider the top features. Among the top 10 features, eight are from advisor notes and two from transaction data. Clearly, features from advisor notes are more important in the model. To validate which data source is more relevant for investor behavior prediction, we also trained models using advisor note features or transaction features alone. The results show that advisor notes give better prediction performance than the transaction data. However, combining both can provide a boost in the model performance. Details are omitted here to conserve space.
In addition to improved investment outcomes and the attainment of financial goals, the value of advice includes a sense of financial well-being achieved through behavioral coaching. Such guidance is particularly valuable during periods of market volatility. Of general interest to the financial advisory industry is a deeper understanding of the nature and impact of the advisor-investor relationship. A detailed understanding helps both advisors and service providers improve investor outcomes.
In the present work, we used the advisor notes to explore the advisor-investor interaction and build a model to predict investor behavior. We used LDA to identify important topics emerging from the unstructured data. The final list of topics included: “Asset allocation review and discussion”, “Client communications”, “Financial literacy education”, “Financial planning”, “Inheritance”, “Family/hobbies”, and “Market discussions”. Two topics: “Financial planning” and “Market discussions” became prominent during March 2020 when the Covid-19 related market volatility was at its peak.
We then trained a Word2Vec model using the advisor notes data to learn an embedded representation of the data that captures domain specific semantic similarity. Using the insights learned from the topic modeling analysis, investor transaction data and a market volatility index (VIX), we constructed a supervised classification model to predict the investors who may require behavioral coaching from a financial advisor.
While an important source of information, advisor notes do have limitations. Advisors may tend to summarize conversations in neutral or positive tones, so this type of analysis may not lend itself to sentiment analysis. The notes may also be represent a selective part of the conversation thus not capturing its true intent. However, the methodology proposed in the present work may be applied to a wide range of data sources also captured during the advisor-investor interaction. These include; survey responses, comments fields, emails, texts, phone call transcripts and social media posts.
In the future, it would be of interest to apply other machine learning techniques to further understand the impact of financial advice on investors’ financial decisions among all advisory models.
The work presented here is a result of a pure and exploratory research work by the authors, and the authors are solely responsible for any mistakes and not The Vanguard Group.
Notes: All investing is subject to risk, including the possible loss of the money you invest. Diversification does not ensure a profit or protect against a loss.
©2021 The Vanguard Group, Inc. All rights reserved.
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