Columbia Business School
New York, NY 10027
NBER Working Papers and Publications
|March 2018||How News and Its Context Drive Risk and Returns Around the World|
with Charles W. Calomiris: w24430
We develop a classification methodology for the context and content of news articles to predict risk and return in stock markets in 51 developed and emerging economies. A parsimonious summary of news, including topic-specific sentiment, frequency, and unusualness (entropy) of word flow, predicts future country-level returns, volatilities, and drawdowns. Economic and statistical significance are high and larger for year-ahead than monthly predictions. The effect of news measures on market outcomes differs by country type and over time. News stories about emerging markets contain more incremental information. Out-of-sample testing confirms the economic value of our approach for forecasting country-level market outcomes.
|May 2001||Asset Prices and Trading Volume Under Fixed Transactions Costs|
with Andrew W. Lo, Jiang Wang: w8311
We propose a dynamic equilibrium model of asset prices and trading volume with heterogeneous agents facing fixed transactions costs. We show that even small fixed costs can give rise to large 'no-trade' regions for each agent's optimal trading policy and a significant illiquidity discount in asset prices. We perform a calibration exercise to illustrate the empirical relevance of our model for aggregate data. Our model also has implications for the dynamics of order flow, bid/ask spreads, market depth, the allocation of trading costs between buyers and sellers, and other aspects of market microstructure, including a square-root power law between trading volume and fixed costs which we confirm using historical US stock market data from 1993 to 1997.
Published: Andrew W. Lo & Harry Mamaysky & Jiang Wang, 2004. "Asset Prices and Trading Volume under Fixed Transactions Costs," Journal of Political Economy, University of Chicago Press, vol. 112(5), pages 1054-1090, October. citation courtesy of
|March 2000||Foundations of Technical Analysis: Computational Algorithms, Statistical Inference, and Empirical Implementation|
with Andrew W. Lo, Jiang Wang: w7613
Technical analysis, also known as charting,' has been part of financial practice for many decades, but this discipline has not received the same level of academic scrutiny and acceptance as more traditional approaches such as fundamental analysis. One of the main obstacles is the highly subjective nature of technical analysis the presence of geometric shapes in historical price charts is often in the eyes of the beholder. In this paper, we propose a systematic and automatic approach to technical pattern recognition using nonparametric kernel regression, and apply this method to a large number of U.S. stocks from 1962 to 1996 to evaluate the effectiveness to technical analysis. By comparing the unconditional empirical distribution of daily stock returns to the conditional distribution c...
Published: Lo, Andrew W., Harry Mamaysky and Jiang Wang. "Foundations Of Technical Analysis: Computational Algorithms, Statistical Inference, And Empirical Implementation," Journal of Finance, 2000, v55(4,Aug), 1705-1765. citation courtesy of