New strategies:
#416 – Using Baltic Dry Index to Trade Tanker Shipping Companies
Period of rebalancing: monthly
Markets traded: equities
Instruments used for trading: stocks
Complexity: Simple strategy
Bactest period: 2013 – 2017
Indicative performance: 3.86%
Estimated volatility: not stated
Source paper:
Michail, Nektarios and Melas, Konstantinos: A Cointegrating Stock Trading Strategy for Tanker Shipping Companies
https://ssrn.com/abstract=3275126
Abstract:
We propose a strategy to trade a portfolio of listed shipping companies in the US market. In particular, we estimate a cointegrating relationship between the weekly stock market returns of a portfolio of tanker shipping companies and the Baltic Tanker Index, exploiting the close relationship between freight rates and the stock market performance of shipping companies. Our results suggest that a trading strategy on the basis of a cointegrating relationship and a simple moving average rule outperforms a standard buy-and-hold strategy in various investment horizons, often by a very wide margin.
#417 – Analyst Days
Period of rebalancing: daily
Markets traded: equities
Instruments used for trading: stocks
Complexity: Simple strategy
Bactest period: 2004 – 2015
Indicative performance: 18.30%
Estimated volatility: 24.08%
Source paper:
Wu, Di and Yaron, Amir: Analyst Days, Stock Prices, and Firm Performance
https://ssrn.com/abstract=3272367
Abstract:
We construct a comprehensive dataset of 3,890 analyst days, which are firm-hosted gatherings where information is disclosed to equity analysts and institutional investors. We demonstrate that firms holding these events have significantly higher abnormal returns after these events, despite the Regulation Fair Disclosure requirement that such information be simultaneously disclosed to the public. A buy-and-hold strategy that holds these stocks for 20 days earns a market-adjusted return of 1.6%, and a similar calendar-time portfolio has a one-month, four-factor alpha of 1.8%. We find no evidence of mean reversion or change in risk exposure after analyst days, and abnormal returns remain significantly positive for up to six months. We classify analyst days into four major types—product announcement, review of results, discussion of strategy, and technology and markets—according to the textual content of their announcements, and we show that product- and market-related analyst days earn significantly higher returns than events reviewing past financial results. Finally, firms holding analyst days have significantly higher revenue growth, earnings per share, and dividend yields up to two years after these events. Analyst coverage, earning estimates, and price targets also increase, and these estimates have lower dispersion. Our results thus suggest that firms use analyst days to convey positive incremental information that has not been incorporated in their stock prices, and market participants significantly underreact to this information.
New research papers related to existing strategies:
#25 – Size Effect
Grabowski: The Size Effect Continues to be Relevant When Estimating the Cost of Capital
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3275366
Abstract:
In this paper, I will review the size effect, potential reasons why one observes the size effect, and correct common misconceptions and address criticisms of the Size Premia (SP). Throughout this paper, I will show that using a pure market factor as the sole risk factor in estimating the expected return provide an incomplete estimate. For the last four decades, research have shown that adjustments to the CAPM are required. I will address some of the criticism to the theoretical basis of the SP and to the application adopted through the CRSP Decile Size Premia and Risk Premium Report – Size Study. Specifically, I demonstrate that the size premium critique by Clifford Ang is not warranted and that the alternative methodology proposed by that author is misleading and cannot be considered as an alternative to the Duff & Phelps’ SP. The methodology the author is proposing picks up statistical errors that he was set to avoid by proposing a variation of Duff and Phelps’ methodology. Finally, I will provide some practical guidance on efficiently and correctly applying SP.
#130 – Investment Factor
Poulsen: Does Debt Explain the Investment Premium?
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3285255
Abstract:
The investment premium — the finding that firms with low asset growth deliver high average returns — is an integral part of recent factor models. I document empirically that the investment premium (1) reflects leverage, (2) does not exist among zero-leverage firms, and (3) increases with firms' refinancing intensities. This new evidence challenges prominent explanations of the investment premium including the q-theory of investment and behavioral finance. To explain the evidence, I develop a model in which firms make both optimal investment and financing decisions. The model shows that the investment premium reflects both leverage and refinancing intensities consistent with my empirical findings.
#304 – Seasonality in Treasury Auctions Strategy
Sigaux: Trading ahead of Treasury auctions
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3290443
Abstract:
I develop and test a model explaining the gradual price decrease observed in the days leading up to anticipated asset sales such as Treasury auctions. In the model, risk-averse investors expect an uncertain increase in the net supply of a risky asset. They face a trade-off between hedging the supply uncertainty with long positions, and speculating with short positions. As a result of hedging, the equilibrium price is above the expected price. As the supply shock approaches, uncertainty decreases due to the arrival of information, investors hedge less and speculate more, and the price decreases. In line with these predictions, meetings between the Treasury and primary dealers, as well as auction announcements, explain a 2.4 bps yield increase in Italian Treasuries.
And two additional related research papers have been included into existing free strategy reviews during last 2 weeks:
#25 – Size Premium
McGee, Olmo: The Size Premium As a Lottery
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3279645
Abstract:
We investigate empirically the dependence of the size effect on the top performing stocks in a cross-section of risky assets separated by industry. We propose a test for a lottery-style factor payoff based on a stochastic utility model for an under-diversified investor. The associated conditional logit model is used to rank different investment portfolios based on size and we assess the robustness of the ranking to the inclusion/exclusion of the best performing stocks in the cross-section. Our results show that the size effect has a lottery-style payoff and is spurious for most industries once we remove the single best returning stock in an industry from the sample each month. Analysis in an asset pricing framework shows that standard asset pricing models fail to correctly specify the size premium on risky assets when industry winners are excluded from the construction of the size factor. Our findings have implications for stock picking, investment management and risk factor analysis.
#77 – Beta Factor in Stocks
Novy-Marx, Velikov: Betting Against Betting Against Beta
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3300965
Abstract:
Frazzini and Pedersen’s (2014) Betting Against Beta (BAB) factor is based on the same basic idea as Black’s (1972) beta-arbitrage, but its astonishing performance has generated academic interest and made it highly influential with practitioners. This performance is driven by non-standard procedures used in its construction that effectively, but non-transparently, equal weight stock returns. For each dollar invested in BAB, the strategy commits on average $1.05 to stocks in the bottom 1% of total market capitalization. BAB earns positive returns after accounting for transaction costs, but earns these by tilting toward profitability and investment, exposures for which it is fairly compensated. Predictable biases resulting from the paper’s non-standard beta estimation procedure drive results presented as evidence supporting its underlying theory.
And a new interesting financial research paper shows that ETF arbitrage mechanism is one of the key channels through which U.S. shocks propagate to local economies leading to increased return correlation with the U.S. market:
Filippou, Gozluklu, Rozental: ETF Arbitrage and International Correlation
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3287417
Abstract:
Assets under management of exchange-traded funds (ETF) have been growing significantly, yet the majority of ETF trades still occur on U.S. exchanges. We show that investment decisions of both institutional and retail investors when trading international country ETFs are mostly driven by shocks related to U.S. fundamentals, measured by VIX, rather than local country risks. Investors react only to negative news about local economies. When U.S. economic uncertainty increases, investors leave the country ETF market and switch to Cash ETF products. We demonstrate that ETF arbitrage mechanism is one of the key channels through which U.S. shocks propagate to local economies leading to increased return correlation with the U.S. market both in time-series and cross-sectional dimensions. We find that countries with stronger ETF price-discovery and lower limits to arbitrage tend to have a higher comovement with the U.S. market.
