The Journal of Investing

Analysis and Comparison of Natural Language Processing Algorithms as applied to Bitcoin Conversations on Social Media.

Benjamin McMillan — 2022-02-12

Bitcoin has matured as an asset of interest over the last decade and so to has the nature of online conversations surrounding it. Given the nature of the asset, specifically its lack of traditional “fundamentals”, it stands to reason that investor sentiment is likely to be a significant portion of the price discovery process. Like bitcoin, social media platforms have emerged over the last decade as a robust marketplace for online discussions. In particular, Reddit has evolved to foster a large number of self-curated conversations around various investing topics. In this paper we focus on two populations within Reddit; the channel (or ‘sub-reddit’) dedicated to bitcoin specifically (‘r/bitcoin’) and the channel dedicated to investing, generally (‘r/investing’). Within each channel, we use four distinct natural language processing algorithms to classify each individual post. For each post, we combine the output of the 4 NLP scores to create an ensemble sentiment score. We then measure the relationship of the ensemble score relative to the price of bitcoin and compare results for each channel. We find that the sentiment scores calculated on conversations within the r/bitcoin sub-reddit show more predictive power than those calculated using the bitcoin-related posts within the r/investing subreddit. Moreover, we find that negative sentiment scores within the r/bitcoin community showed a robust tendency to mark local troughs in the price of bitcoin that were followed by a significant increase in the average excess return of bitcoin in the days immediately following (i.e. a “capitulation” indicator).

Applying News sentiment for Optimizing Strategic Asset Allocations

Matthias W. Uhl — 2022-02-12

We show that it is possible to enhance traditional Black and Litterman (1992) strategic asset allocation (SAA) models with a behavioral-based approach based on news sentiment. In an out-of-sample backtest over ten years, the news sentiment-based SAA outperforms the benchmark SAA by 0.5% p.a. with less risk and a 20\ higher Sharpe Ratio. The news sentiment data are also statistically different from price momentum measures.

Value against Glamour stocks:

Raul Leote de Carvalho — 2022-02-12

Value stocks endured a severe under-performance in recent years leading many investors to question the relevance of value investing or even to consider its demise. However, in this paper we show that the spread between the valuations of value stocks compared with those of their most expensive peers has been expanding in all regions and macro-sectors. By the end of 2020, these value spreads reached the same extreme high levels last seen at the peak of the Tech Bubble in 2000. The probability that value spreads compress going forward is now the highest since the last peak of value spreads 20 years ago. Moreover, we show that value stocks and multifactor strategies tend to out-perform in periods of value spread compression. We thus believe that capitulating on value investing or exiting multifactor strategies may turn out to be a costly decision.

Riding the 1/N Premium

Lars Kaiser — 2022-02-12

We propose a simple and effective rotation strategy. The strategy rotates between the value-weighted market portfolio (VW) and the equal-weighted counterpart (EW) based on an implicit market signal – the lagged one-month market return. We report a statistically significant relation between the lagged one-month market return and the future 1/N premium, which is the return difference between a VW and EW portfolio. Building on the predictive quality and exploiting the time-varying nature of the 1/N premium, we introduce a transparent and robust investment strategy that yields superior absolute and risk-adjusted returns.

Cutting Through the Fog of Asset Class Labels

Richard M. Ennis — 2022-02-12

N/a

The Information in Low Forecasts

Haim A. Mozes — 2022-02-12

This paper introduces an intuitive, earnings-based sentiment indicator that is useful for forecasting overall market direction. The indicator aggregates the difference between the mean and the low forecasts on individual stocks, with the rationale being that the indicator captures for the entire market what dispersion captures for a single stock. The basic results are that when the aggregate low forecast is considerably lower than the aggregate mean forecast, subsequent forecast revisions tend to be more negative and future index returns tend to be lower and more volatile. However, when the aggregate low forecast is extremely low compared to the aggregate mean forecast, investor and analyst sentiment tends to be overly pessimistic, and the market therefore tends to perform strongly.

Tax Reform, Firm Value, and Biden Proposals

Robin Overweg — 2022-02-12

ABSTRACT

Federal income tax legislative reforms impact both investors and corporations. It is prudent to understand these legislative effects before we experience the next round of tax legislation. The present research examines whether announcements regarding the passage of the Tax Cuts and Jobs Act of 2017 affected publicly traded firm value. Multiple-date event study methodology is utilized to detect whether abnormal returns were present in analysis of S&P 500 companies after various announcements of the Tax Cuts and Jobs Act’s passage through Congress. Results on many of the event dates provide support that the Tax Cuts and Jobs Act had a positive impact on firm value. Differences are also analyzed in firms based on dividend practices, multinationality, revenue growth, and effective tax rates. These results provide valuable insight as U.S. corporations and investors prepare for the possibility of corporate tax changes from the Biden administration.

Intangible ironies: investor mispricing of company assets on and off its balance sheet

Sanne DeBoer — 2022-02-12

We examine how investors evaluate the mix of company assets both on and off its balance sheet. On aggregate, they appear to correctly value tangible assets but misprice intangible assets, which have increased in economic importance. In particular, investments in stakeholder capital such as innovation, brand, and employees often go unrecognized both on the balance sheet and by investors. In contrast, the premium paid for past acquisitions which is included on the financial statements as goodwill generally fails to deliver on expectations, being written down too slowly by management and shareholders alike. Corroborating a recent surge of papers on this topic, we find that adjusting valuation metrics for the actual benefit of such intangibles leads to better performance thereof in global equity markets. More impactfully, investors can diversify value exposure by targeting companies with latent such growth assets. Our findings suggest market efficiency would be served by better accounting standards for intangible assets, allowing more flexibility on what types of investment may be capitalized while simultaneously tightening rules around impairment and amortization.

Commentary:

George Frankfurter — 2022-02-12

We live in a new reality, its full consequences unexplored thus far. Lying is not just tolerated sometimes and later forgiven as in the past, but an imperative, demanded by the masses.