positioning on Ray Yang, Ph.D.

the Positioning of Stock Fundamentals and Abnormal Stock Returns

Tue, 19 May 2020 00:00:00 +0000

1 Motivation

According to Warren Buffet, buying a stock is like “acquiring an ownership interest in the business of a company.” Buffet tends to only care about the fundamentals of the stocks he buys.

Here the question is: do companies have a “fundamental positioning” – a relative standing to peer firms in terms of the financial fundamentals — to determine the differences in stock returns?

As a strategist, I consider a stock as a winner only when it beats the market. In other words, I only care about the “winner premium” — the stock returns above the expected level (a.k.a. above-normal rate of returns). I will explore utilizing the “fundamental positionings” to pick (predict) winner stocks.

2 Pipeline

To implement this idea, I used XGBoost (an implementation of gradient boosted decision trees), which requires minimal feature engineering/selection, to predict the “buy-and-hold” abnormal returns using the fundamental positionings prior to the holding period (90 days). To examine the predictive power of fundamental positionings, I left the base financial ratio variables along with the fundamental positionings (the relative percentile rank within an industry based on these ratios) in the feature space. After finding the best model from bayesian hyperparameter tuning, we can then evaluate the model performance and see the relative importance of fundamental positioning features and the financial ratios.

The data is streamed from “ Tushare,” which provides free access to the SH/SZ stock exchange.

3 Result

In the above graph, the points in yellow are winners that beat the market, whereas the points in purple are “losers” that fail to beat the market. If we use the algorithm to pick the top three winners, I will indeed generate positive abnormal returns for us. The mean squared error of the model is 0.019.

This graph shows the relative importance of the features. I added a prefix “R” (relative standing) to the variable names to denote the “fundamental positioning” features. Around half of these important features are fundamental positionings. This shows the positioning (which is based on the percentile ranking of a focal company relative to industry peers) does contribute to the prediction of stock market returns, in addition to the financial ratios (which does not involve firm-to-firm comparisons).

Tracking the Change of Industry Positioning Caused by an Acquisition

Sun, 19 Apr 2020 00:00:00 +0000

1. Motivation

Due to enduring resource heterogeneity, firms tend to hold persistent positionings within an established industry (like commercial banking). However, transformative corporate actions may alter a firm’s asset structure and create a new industry for the acquirer. Thus, we can map out the industry positions and track the position change caused by an acquisition.

2. Mapping the Positioning and Tracking the Movement

2.1 the Biplot

Consider commercial banks as an example. Different banks may hold differently structured financial asset portfolios and occupy different positions. We can assess a bank’s position based on the proportion of cash, commercial loans, mortgage loans, etc., as a percentage in the total asset (i.e., multiple dimensions). A biplot can provide us a quick intuition about how those dimensions relate to one another. However, the relative positions shown on biplot would not be accurate due to the loss of information in dimension reduction.

2.2 the 3-D and 2-D Scatterplots based on MDS

In an alternative approach, we can build distance-based features by exploring the inter-firm distance using multi-dimensional scaling (MDS). On Feb. 12th, 2018, Pacific Premier Bancorp launched an acquisition of Grandpoint Capital. Using this acquisition event as an example, we can first calculate the interfirm distance using MDS and then track how the acquisition shifts a bank’s position (in both a 3-D and 2-D scatter plots).

the Positionings of Big Tech Firms on Industry Presence and Competition Description

Thu, 16 Apr 2020 00:00:00 +0000

In this post, I calculated the positioning of big tech firms based on two metrics, the multiple-industry presence (based on 3-digit SIC codes) versus the competition description (in 10-K filings), and compared the difference between the two approaches.

1 the positioning by industry presence across SIC categories

Scholars have used SIC codes to understand interfirm differences ¹ Big public firms typically straddle across multiple SIC industry categories. So it is straightforward and easy to see their positionings by multiple-industry participation. Accordingly, a firm’s position can be calculated as the sales distribution across multiple SIC industry categories ².

2 the positioning by competition description in 10-K filing

The SIC-code might be “backward-looking,” as firms tend to quite if particular combinations of multiple-industry business lines create economic inefficiencies. Only efficient multi-industry presence survives.

In contrast, the SEC filings may reflect managers’ current understanding of what they do. Especially when they characterize their competition, managers tend to draw their forward-looking expectations on the various forms of competitive threats. Some scholars³ and companies⁴ have used the text-based approach to calculate inter-industry distance.

2.1 calculating positioning based on the competition description

I collected the competition description texts of the largest 17 tech companies. Below are some frequently used words in those statements.

I ran an LDA topic modeling with 11 latent topics, which yields a low perplexity score.

The figure below shows how the 11 topics distribute across terms.

This is the topic loading on the 11 firms’ competition description, which is used to calculate the pairwise distance.

2.2 the final result: positioning by competition description

Here is the positioning map based on the competition description. The graph on the right shows the positioning of American companies. Looking at the results, we can see some interesting differences. The competition description moves some firms closer or further away from one another as compared to the SIC-presence positioning map.

for example:
Teece, D. J., Rumelt, R., Dosi, G., & Winter, S. (1994). Understanding corporate coherence: Theory and evidence. Journal of economic behavior & organization, 23(1), 1-30.

Lien, L. B., & Klein, P. G. (2013). Can the survivor principle survive diversification?. Organization Science, 24(5), 1478-1494.
↩
Litov, L. P., Moreton, P., & Zenger, T. R. (2012). Corporate strategy, analyst coverage, and the uniqueness paradox. Management Science, 58(10), 1797-1815.Lien, L. B., & Klein, P. G. (2013).↩
https://hobergphillips.tuck.dartmouth.edu ↩
Morningstart’s Company Comparables System ↩

Mapping Twitter Topics and Sentiment about FAAMG Companies since Covid-19 Outbreak

Wed, 15 Apr 2020 00:00:00 +0000

1 Motivation

On a whim, I mapped out what people say (the text) and feel (the sentiment) about the top tech companies (FAAMG) on twitter since the Covid-19 outbreak. The keywords and sentiment that are associated with the main topics should differ across these companies.

By no means I am drawing any causal association here. But the story starts with checking the recent price movements of the FAAMG stocks. Obviously, Covid-19 has impacted these companies quite differently.

2 Procedures

2.1 Data Source

For each company, I downloaded 1,000 tweets containing the company name as the hashtag (e.g. “#amazon” for Amazon). I used Python module “tweepy” to stream the data and set the “since” parameter to ‘2020-03-01’, which collects a sample of 1,000 tweets since March 1st.

2.2 Topic Modeling

Using Python module “gensim”, I detected three most significant topics (including Covid-19, business, and daily life), which are named by the associated keywords (most relevant terms).

Using Python module “vaderSentiment”, I computed sentiment scores for each tweet and aggregated the scores by company. The topic-based sentiment scores are used to construct the perceptual map, in which each company takes a different position. Using Python module “nltk”, I find the most frequent topic-based co-occurring words for each company.

3 Results

3.1 Perceptual Map of FAAMG Based on Topic Sentiment

It looks Amazon does well on all topics, especially on Covid-19-related topic. Microsoft and Apple do well on “Business”.

3.2 Keywords Co-occurrence Network on Each Topic

Here are the work keywords co-occurrence patterns for each topic of these companies.

A Summary of Recent 'Social Evaluation' Research

Mon, 25 Mar 2019 00:00:00 +0000

1 Motivation

The goal of this post is to analyze and review the “social evaluation” research tracing back to Zuckerman (1999)¹. The analysis/review is based on hand-collected papers that directly and indirectly cite Zuckerman (1999). The review is not intended to be comprehensive but to understand the central ideas on the topic developed by management/sociology scholars in the past 20 years.

Let’s continue on the “positioning” topic from one of my previous posts (firms are social actors being evaluated by social audiences), but shift our focus of discussion from “what actors do” to “what audience think.” That is, instead of an “actor” standpoint, let’s take an “audience” perspective to consider how firms as market participants are evaluated by market audiences. Reality doesn’t directly translate into human decisions; human beings can only understand reality from their brain, which is subjected to social construction. As a result, the evaluations on market participants do not come from complete rationality in a social vacuum. Rather, audiences’ cognition is shaped by the market’s categorical structure and/or the socially accepted categorical beliefs.

2. Biblio-Analysis

2.1 Selection Criteria

## Warning: The curvature argument has been deprecated in favour of strength

The raw data is based on 29 hand-collected papers directed and indirectly citing Zukerman (1999) and extracted from Web of Science. The papers that not on the topic of “social evaluation” are excluded from the sample, based on the author’s judgment. We can get a general idea of the citation relationship among those papers from the direct citation network graph.

2.2 Central Sources and Keywords

By quickly checking the origins (papers being cited) of the sampled papers, we can see that a big portion (the blue-colored cluster) is the classic institution work. It makes sense, as the discussion of the underlying mechanism of social evaluation should be related to how the evaluating standards are formed in the institutionalization processes. The summary in 3.2 will be based on the top-cited “social evaluation” papers in the red-colored cluster.

The inter-connected keyword clusters present the relationship among the main topics covered by this stream of research. It seems to have a quite strong “strategy” flavor². The most frequently used keywords include “industry”, “market”, and “performance.”

3 Classic Papers

3.1 Top Citations

by frequency:

## [1] "ZUCKERMAN EW, 1999, AM J SOCIOL, V104, P1398, DOI 10.1086/210178"          
## [2] "HSU G, 2006, ADMIN SCI QUART, V51, P420, DOI 10.2189/ASQU.51.3.420"        
## [3] "HSU G, 2009, AM SOCIOL REV, V74, P150, DOI 10.1177/000312240907400108"     
## [4] "PONTIKES EG, 2012, ADMIN SCI QUART, V57, P81, DOI 10.1177/0001839212446689"
## [5] "RAO H, 2005, AM SOCIOL REV, V70, P968, DOI 10.1177/000312240507000605"

by co-citation centrality:

## [1] "most central sources (top 5)"

## ZUCKERMAN EW 1999        HSU G 2009      HSU G 2006-1  PONTIKES EG 2012 
##         1.0000000         0.7142248         0.6970316         0.6754593 
##        RAO H 2005 
##         0.6150981

The most frequent (by frequency count) citations are also the most central (by eigenvector centrality) citations.

3.2 Summary of Top-cited Papers

Hsu et al. (2009) provide an integrative explanation for the multi-category disadvantage. The paper combines the actor- and audience-centered explantions and argues that category spanning is both difficult for the actor to manage and hard for the audience to understand. Hsu (2006) explicitly analyzes the underlying cognition mechanism in which film audiences encounter difficulties in making sense of multi-genre films. Pontikes (2012) analyzes the different segments of market audiences with distinct preferences for ambiguous classification–the difficulty of classifying startups into established categories. According to her, consumers and corporate venture capitalists are more passive market-takers who prefer startups that are easy to classify; in contrast, individual venture capitalists are market-makers who have the power to redefine the market structure and see ambiguous classification as opportunities. Rao et al. (2005) argue that the market boundaries are melting when high-status market participants’ cross the market categories–French chefs with starts in the Guide Michelin make hybrid cuisines. Typically, there are cost and risk associated with “cross-category borrowings.” But they tend to attenuate when borrowings prevail. Overall, it seems to be risky to cross the categorical boundaries. But contextual contingencies for the benefit/risk profile of category-straddling exists.

References:

Zuckerman, E. W. (1999). The categorical imperative: Securities analysts and the illegitimacy discount. American Journal of Sociology, 104(5), 1398-1438.

Hsu, G., Hannan, M. T., & Koçak, Ö. (2009). Multiple category memberships in markets: An integrative theory and two empirical tests. American Sociological Review, 74(1), 150-169.

Hsu, G. (2006). Jacks of all trades and masters of none: Audiences’ reactions to spanning genres in feature film production. Administrative Science Quarterly, 51(3), 420-450.

Pontikes, E. G. (2012). Two sides of the same coin: How ambiguous classification affects multiple audiences’ evaluations. Administrative Science Quarterly, 57(1), 81-118.

Rao, H., Monin, P., & Durand, R. (2005). Border crossing: Bricolage and the erosion of categorical boundaries in French gastronomy. American Sociological Review, 70(6), 968-991.

The central idea of this paper is that firms’ stock price is affected by the categorical pattern of financial analysts’ industry coverage, which reflects the institionalized expectations for firms’ product mix straddling across various market categories.↩
For the central topics in strategy research, refer to “the intellectual structure of the strategic management field…” by Nerur et al. (2008) and “what is strategic management…” by Nag el al. (2007)↩

A Quick Bibliometric Analysis on “Positioning”

Tue, 26 Feb 2019 00:00:00 +0000

I had a whim and wanted to do some quick bibliometric analysis on the topic of “strategic positioning”.

The raw data is extracted from Web of Science. The search parameters:

TITLE: ("strategic positioning" OR "industry positioning" OR "market positioning")
Refined by: WEB OF SCIENCE CATEGORIES: ( MANAGEMENT OR BUSINESS OR ECONOMICS ) AND WEB OF SCIENCE CATEGORIES: ( MANAGEMENT OR BUSINESS OR INTERNATIONAL RELATIONS OR ECONOMICS ) AND DOCUMENT TYPES: ( ARTICLE ) AND LANGUAGES: ( ENGLISH )
Timespan: All years. 
Indexes: SCI-EXPANDED, SSCI, A&HCI, CPCI-S, CPCI-SSH, BKCI-S, BKCI-SSH, ESCI, CCR-EXPANDED, IC.

I used the R package “bibliometrix” to conduct (1) “bibliographic coupling” and (2) “co-citation” analysis.

Bibliographic coupling analysis allows us to explore the “confluence and interactions” among recent papers. Two articles are bibliographically coupled if they co-cited at least one article.

Co-citation analysis allows us to trace back to the origin of thoughts and classic papers. Two articles have a co-citation tie if they are both cited in a third article.

Reference:

Aria, M. & Cuccurullo, C. (2017) bibliometrix: An R-tool for comprehensive science mapping analysis, Journal of Informetrics, 11(4), pp 959-975, Elsevier.

Optimal Positioning for Firms and Individuals

Mon, 25 Feb 2019 00:00:00 +0000

Imagine you are invited to a party. You want to get noticed by other partygoers. Or you want to be recognized as a “cool” person by those who you are interested in. The “coolness” will result in some social benefits you desire. This is a scene of “social evaluation,” in which you want to stand out of the crowd of peer actors (competitors) to impress the audiences (customers and suppliers). If you succeed, the peer actors will admire you, the audiences will adore you, and of course, the esteems and profitable relationships will follow. Now the question is, “how to position yourself to be ‘cool’ so that you can win the game?”

Practically, the first question coming to your mind might be “to what extent should I be similar to or different from others?” You want to be different so that you can stand out. However, you don’t want to be too different so that people think you are wired. Your goal is to “impress” but not to “surprise.” It is a bad idea to violate the audience’s expectations on your appearances and behaviors – the commonly accepted “norms.” Violations typically lead to penalties.

Excessive differentiation is risky. It jeopardizes your “legitimacy,” the potential for being accepted by the audience, who controls the resources and opportunities. Now the headache you get is — being similar places you under competitive pressures (so you can’t stand out) but being different gives you the risk of losing legitimacy. Then, you may find a simple solution: find a middle ground between the two extremes — “to be different as ‘legitimately’ possible.” Good. You got a “holistic” approach, in which you consider all the audiences, attributes, and evaluation processes as a “whole.” And you have a clear goal—maximizing the total audience perceptions. It is implementable. It is the wisdom of Confucius’ as well as Aristotle’s “Golden Mean.”

If you don’t want to stop here, let’s think further. We can do some analysis¹, by moving away from the “holistic” approach to an “analytical” one. For example, we can anatomize the positioning problem in three ways: (1) separating the evaluating audiences, (2) separating the actor attributes, and (3) separating the evaluation processes.

First, different audience groups (government, customers, investors, etc.) may demand different levels of conformity vs. differentiation, so you can deploy specific positioning strategy to target specific audiences (e.g. horizontal websites tend to give different people different things while vertical websites tend to meet specific people’s specific needs). Second, as audiences allocate their decision weights among multiple dimensions of difference, you can conform on most of them to maintain legitimacy and then differentiate on a couple of others to look cool (e.g. in a party, people always care about dress code while worrying about outfit clash). Third, as the audiences typically engage in a categorization process before making the final selection, you can strategize for “entering the game” and “winning the game” separately (e.g. audiences tend to make a short list first, select the winner from it. No matter you want to design a website or find a job, to make it to the shortlist, you need to first figure out the similarities that help you stay relevant; and in the selection stage, find the differences that help you win).

The positioning problem shouldn’t stop here. As audiences evaluate actor attributes with their subjective minds, numerous factors (cognitive, sociological, cultural ones) determine how individual opinions aggregate and emerge as market behavior. Researchers have been studying the patterns, drawing inferences and making predictions. Importantly, there could exist positive and negative feedback loops for the mutual influence between actor behavior (strategic action) and audience perception (performance evaluation). This is what I’m interested in.

References:

Deephouse, D. L. 1999. To be different, or to be the same? It’s a question (and theory) of strategic balance. Strategic Management Journal, 20(2): 147–166.

Zuckerman, E. W. 2016. Optimal distinctiveness revisited: An integrative framework for understanding the balance between differentiation and conformity in individual and organizational identities. Handbook of Organizational Identity.

Zhao, E. Y., Fisher, G., Lounsbury, M., & Miller, D. 2017. Optimal distinctiveness: Broadening the interface between institutional theory and strategic management. Strategic Management Journal, 38(1): 93–113.

Merriam-Webster has a definition of “analysis” — “separation of a whole into its component parts.”↩︎