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1. A two-stage Bayesian network model for corporate bankruptcy prediction

Author:Cao, Y;Liu, XQ;Zhai, J;Hua, S

Source:INTERNATIONAL JOURNAL OF FINANCE & ECONOMICS,2020,Vol.

Abstract:We develop a Bayesian network (LASSO-BN) model for firm bankruptcy prediction. We select financial ratios via the Least Absolute Shrinkage Selection Operator (LASSO), establish the BN topology, and estimate model parameters. Our empirical results, based on 32,344 US firms from 1961-2018, show that the LASSO-BN model outperforms most alternative methods except the deep neural network. Crucially, the model provides a clear interpretation of its internal functionality by describing the logic of how conditional default probabilities are obtained from selected variables. Thus our model represents a major step towards interpretable machine learning models with strong performance and is relevant to investors and policymakers.
2. Estimating price impact via deep reinforcement learning

Author:Cao, Y;Zhai, J

Source:INTERNATIONAL JOURNAL OF FINANCE & ECONOMICS,2020,Vol.

Abstract:Price impact is the adverse change of the asset price against trader's action. As a crucial part of the indirect trading cost, price impact has attracted increasing attention in both econometric and data science literature. In this paper, we draw upon both strands of the literature and develop a deep neural network enhanced recursive (DeRecv) model to estimate temporary and permanent price impact of an order or trade. The temporary price impact is calculated as the sum of the expected immediate impact at each time point after taking action in an ad hoc market condition. The permanent price impact is defined as a new permanent level at which the information of the incoming order is entirely absorbed by the market. Through the experimental evaluation based on data from 10 stocks at NASDAQ and Shanghai Stock Exchange, we show that the proposed DeRecv model is better than the reinforcement learning model and the traditional vector autoregressive model.
3. TRANSACTIONS COSTS, INDEX ARBITRAGE AND NON-LINEAR DYNAMICS BETWEEN FTSE100 SPOT AND FUTURES: A THRESHOLD COINTEGRATION ANALYSIS

Author:Tao, J;Green, CJ

Source:INTERNATIONAL JOURNAL OF FINANCE & ECONOMICS,2013,Vol.18

Abstract:We apply threshold cointegration to study the dynamics between the London FTSE100 spot index and its futures price, using percentage mispricing as the threshold variable to identify the no-arbitrage band. Estimated asymmetries in the band suggest that short sale restrictions in the spot market represent a hurdle for arbitrage. Factors other than transactions costs did not affect the width of the no-arbitrage band but did affect the price dynamics more directly. The evidence supports a conclusion that LSE SETS (1997) and LIFFE CONNECT (1998) trading systems reduced transactions costs and hence the width of the no-arbitrage band. Copyright (c) 2011 John Wiley & Sons, Ltd.
Total 3 results found
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