Foundations of Technical Analysis: Computational Algorithms, Statistical Inference, and Empirical Implementation

Technical analysis, also known as "charting", has been a part of financial practice for many decades, yet little academic research has been devoted to a systematic evaluation of this discipline. 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 US stocks from 1962 to 1996 to evaluate the effectiveness of technical analysis. By comparing the unconditional empirical distribution of daily stock returns to the conditional distribution---conditioned on specific technical indicators such as head-and-shoulders or double-bottoms---we find that over the 31-year sample period, several technical indicators do provide incremental information and may have some practical value.

New News is Bad News

An increase in the novelty of news predicts negative stock market returns and negative macroeconomic outcomes over the next year. We quantify news novelty - changes in the distribution of news text - through an entropy measure, calculated using a recurrent neural network applied to a large news corpus. Entropy is a better out-of-sample predictor of market returns than a collection of standard measures. Cross-sectional entropy exposure carries a negative risk premium, suggesting that assets that positively covary with entropy hedge the aggregate risk associated with shifting news language. Entropy risk cannot be explained by existing long-short factors

Credit Information in Earnings Calls

We develop a novel technique to extract credit-relevant information from the text of quarterly earnings calls. This information is not spanned by fundamental or market variables and forecasts future credit spread changes. One reason for such forecastability is that our text-based measure predicts future credit spread risk and firm profitability. More firm- and call-level complexity increase the forecasting power of our measure for spread changes. Out-of-sample portfolio tests show the information in our measure is valuable for investors. Both results suggest that investors do not fully internalize the credit-relevant information contained in earnings calls

Asset Prices and Trading Volume Under Fixed Transactions Costs

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.

Essays in capital markets

Thesis (Ph.D.)--Massachusetts Institute of Technology, Sloan School of Management, 2000.Includes bibliographical references.The first two chapters of this dissertation study financial asset markets which are not "frictionless." The first chapter focuses on the effects of transaction costs. The second chapter focuses on the interaction between asymmetric information and strategic behavior. The third chapter empirically assesses the informativeness of certain types of price indicators based on technical analysis. In Chapter 1 ( co-authored with Andrew Lo and Jiang Wang) we propose a dynamic equilibrium model of asset pricing and trading volume with heterogeneous investors facing fixed transactions costs. We show that even small fixed costs can give rise to large "notrade" regions for each investor'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 buyer and seller, 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. Chapter 2 develops an equilibrium model of a dynamic asymmetric information economy. The model is solved under two circumstances: where the informed and uninformed sectors are both competitive, and where the informed sector is competitive and the uninformed sector consists of a single, strategic agent. The strategic uninformed agent, when facing the same signals as the uninformed competitive sector, manages to extract different information abo~t the state of the economy. I find that expected returns, return variability, and unexpected trading volume differ between the competitive and the strategic economies. Furthermore, this difference depends on the degree of informational asymmetry between the two sectors. In the strategic economy, less surplus is lost due to informational arbitrage by the informed sector. Interestingly, the presence of asymmetric information allows even the competitive uninformed agents to gain surplus from allocational trade. Finally, I examine the incentives of agents to become better informed, and find that sometimes both competitive and strategic agents are better off under worse information. Technical analysis, also known as "charting," has been a 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 Chapter 3 ( co-authored with Andrew Lo and Jiang Wang), 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 of technical analysis. By comparing the unconditional empirical distribution of daily stock returns to the conditional distribution-conditioned on specific technical indicators such as head-and shoulders or double-bottoms-we find that over the 31-year sample period, several technical indicators do provide incremental information and may have some practical value.by Harry Mamaysky.Ph.D

Interest Rates and the Durability of Consumption Goods

In this article I study an economy with irreversible durable investment and investors who consume a durable and a nondurable good. In equilibrium, these assumptions lead to endogenous variation in the implied risk aversion of investors. This in turn causes the short term interest rate, the exchange rate between the two goods, and the term structure of interest rates to fluctuate stochastically. I investigate the connection between these financial variables and investment into the durable good, and provide one explanation for several features of the empirical relationship between interest rates and the business cycle. Additionally, I derive a closed form asymptotically valid solution of the model. Usin

Interest Rates and the Durability of Consumption Goods

In this article I study an economy with irreversible durable investment and investors who consume a durable and a nondurable good. In a general equilibrium setting, these assumptions lead to endogenous variation in the implied risk aversion of investors and in the term structure of interest rates. In the model, the magnitude of the intertemporal elasticity of substitution places certain restrictions on the joint dynamical behavior of durable consumption, nondurable consumption, and the yield curve. Tests of the model using postwar U.S. data are supportive of these restrictions. However, while the model is able to generate a relatively large term spr