New Research: Sentiment, Expectations, and Stat Arb

Some interesting articles have been added to the forthcoming list at Quantitative Finance. Cites and abstracts are below, with links to preprints where available. I don’t have time to add commentary at the moment, but am happy to answer questions in the comments section.

Abel Rodriguez & Enrique Ter Horst, “Measuring expectations in options markets: an application to the S&P500 index.”

Extracting market expectations has always been an important issue when making national policies and investment decisions in financial markets. In options markets, the most popular way has been to extract implied volatilities to assess the future variability of the underlying asset with the use of the Black-Scholes formula. In this manuscript, we propose a novel way to extract the whole time varying distribution of the market implied asset price from option prices. We use a Bayesian non-parametric method that makes use of the Sethuraman representation for Dirichlet processes to take into account the evolution of distributions in time. As an illustration, we present an analysis of options on the S&P500 index.

Haiqiang Chen, Terence Tai-Leung Chong, & Xin Duan, “A principal-component approach to measuring investor sentiment.”

This paper develops a new composite sentiment measure for the stock market of Hong Kong. Factors under consideration include the short sell volume, the Hong Kong Inter-bank Offered Rate (HIBOR), Relative Strength Index (RSI), Money Flow Index (MFI), the performances of the US and the Japanese equity markets, and market turnover. Using the new sentiment measure, a regime switching threshold model for the Hang Seng Index returns is estimated and a profitable sentiment-based trading rule is proposed.

Marco Avellaneda & Jeong-Hyun Lee, “Statistical arbitrage in the US equities market.”

We study model-driven statistical arbitrage in US equities. Trading signals are generated in two ways: using Principal Component Analysis (PCA) or regressing stock returns on sector Exchange Traded Funds (ETFs). In both cases, the idiosyncratic returns are modelled as mean-reverting processes, which leads naturally to ‘contrarian’ strategies. We construct, back-test and compare market-neutral PCA- and ETF-based strategies applied to the broad universe of US equities. After accounting for transaction costs, PCA-based strategies have an average annual Sharpe ratio of 1.44 over the period 1997 to 2007, with stronger performances prior to 2003. During 2003-2007, the average Sharpe ratio of PCA-based strategies was only 0.9. ETF-based strategies had a Sharpe ratio of 1.1 from 1997 to 2007, experiencing a similar degradation since 2002. We also propose signals that account for trading volume, observing significant improvement in performance in the case of ETF-based signals. ETF-strategies with volume information achieved a Sharpe ratio of 1.51 from 2003 to 2007. The paper also relates the performance of mean-reversion statistical arbitrage strategies with the stock market cycle. In particular, we study in detail the performance of the strategies during the liquidity crisis of the summer of 2007, following Khandani and Lo [Social Science Research Network (SSRN) working paper, 2007].