Search Frictions in Financial Markets
Paper Session
Saturday, Jan. 6, 2024 10:15 AM - 12:15 PM (CST)
- Chair: Semih Üslü, Johns Hopkins University
Comparing Search and Intermediation Frictions Across Markets
Abstract
In intermediated markets, trading takes time and intermediaries extract rents. We estimate a structural search-and-bargaining model to quantify these trading delays, intermediaries’ ability to extract rents, and the resulting welfare losses in government and corporate bond markets. Using transaction-level data from the UK, we identify a set of clients who are active in both markets. We exploit the cross-market variation in the distributions of these clients’ trading frequency, prices, and trade sizes to estimate our structural model. We find that trading delays and dealers’ market power both play a crucial role in explaining the differences in liquidity across the two markets. Dealers’ market power is more severe in the government bond market, while trading delays are more severe in the corporate bond market. We find that the welfare loss from frictions in the government and corporate bond markets are 7.8% and 12.2%, respectively, and our decomposition implies that this loss is almost exclusively caused by trading delays in the corporate bond market, while trading delays and dealers’ market power split the welfare loss equally in the government bond market. Using data from the COVID-19 crisis period, we also find that these welfare losses might more than triple during turbulent times, revealing the fragility of the OTC market structure.Technical Analysis with Machine Learning Classification Algorithms: Can it Still ‘Beat’ the Buy-and-hold Strategy?
Abstract
This paper undertakes an extensive study to search for empirical evidence of directional predictability and profitability on an aggregate stock market index by applying supervised machine learning (ML) algorithms to a large set of financial variables, technical indicators, and price patterns to generate predictions [of the moving direction of future stock price] that lead to the most profitable trading strategy. We use both symmetric and asymmetric loss function to train (and both statistical and economic scoring functions to cross-validate) a ML algorithm. We also extend the bootstrap Reality Check (RC) procedure to formally compare the performance of trading methods.The trading strategy using one-period ahead forecasts can generate higher annualized returns than the buy-and-hold strategy when transaction cost is low. Most positive annualized excess returns (i.e., the annualized returns of our strategy in excess of those from the buy-and-hold strategy) are realized during trading sessions with high volatility. However, the trading strategy using multiple-days ahead forecasts can become less profitable. There is a strong evidence that some scoring functions used to cross validate a ML algorithm can generate more economically significant predictions than the others. Several candlestick chart patterns have a strong predictive power that can be effectively leveraged by Random Forest to increase annualized excess returns compared to using only financial variables and technical indicators as predictors.
Keywords: Machine learning, Technical indicators, Price patterns, Directional predictability, Trading strategy
JEL classification: C53, C58, G11, G17
Asymmetric Information and the Liquidity Role of Assets
Abstract
Monetary models overwhelmingly feature a particular asset, "money", as the medium of exchange in an economy. However, the adoption of a medium of exchange is endogenous and subject to changes if conditions favor a different asset. We study the liquidity role of a real asset that is subject to asymmetric information. We find that rather than using the asset directly as a medium of exchange, agents prefer to liquidate it for money in a secondary asset market, thus establishing money as the dominant medium of exchange. Furthermore, we show that a decrease in severity of asymmetric information in the secondary asset market can hurt welfare. Finally, we find that inflation is crucial for the determination of key equilibrium variables, such as the volume of trade in the secondary asset market, and the decision of agents to invest in information that reduces the degree of information asymmetry.JEL Classifications
- D8 - Information, Knowledge, and Uncertainty
- G1 - General Financial Markets