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).

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 ↩

big firms on Ray Yang, Ph.D.