CAN LEADING BUSINESS CYCLE INDICATORS PREDICT THE DIRECTION OF THE SOUTH AFRICAN COMMERCIAL SHARE PRICE INDEX?

Leading indicators are a popular way to predict turning points in the business cycle. However, since the lead time of these indicators differ, those with a longer lead could potentially also be used to predict turning points in other leading indicators. This paper empirically explores the viability of using leading indicators to predict the turning points of an index of commercial shares on the JSE Securities Exchange. Although share prices are leading the business cycle, other leading indicators that lead the business cycle by a longer period should lead share prices and, therefore, could potentially be useful in predicting the direction of share price movements. The objective of this study is to evaluate and compare the performance of different leading indicators in leading the commercial share price index and in predicting turning points in the commercial share price index. In addition, a multivariate logit model is developed and estimated using these leading indicators in an attempt to improve the accuracy of forecasting the direction of the commercial share index.     

Learning about Consumption Dynamics

This paper characterizes U.S. consumption dynamics from the perspective of a Bayesian agent who does not know the underlying model structure but learns over time from macroeconomic data. Realistic, high‐dimensional macroeconomic learning problems, which entail parameter, model, and state learning, generate substantially different subjective beliefs about consumption dynamics compared to the standard, full‐information rational expectations benchmark. Beliefs about long‐run dynamics are volatile, with counter‐cyclical conditional volatility, and drift over time. Embedding these beliefs in a standard asset pricing model significantly improves the model's ability to match the stylized facts, as well as the sample path of the market price‐dividend ratio.     

Lender Automation and Racial Disparities in Credit Access

Process automation reduces racial disparities in credit access by enabling smaller loans, broadening banks' geographic reach, and removing human biases from decision making. We document these findings in the context of the Paycheck Protection Program (PPP), where private lenders faced no credit risk but decided which firms to serve. Black-owned firms obtained PPP loans primarily from automated fintech lenders, especially in areas with high racial animus. After traditional banks automated their loan processing procedures, their PPP lending to Black-owned firms increased. Our findings cannot be fully explained by racial differences in loan application behaviors, preexisting banking relationships, firm performance, or fraud rates.     

Stock Market Liquidity and the Business Cycle

In the recent financial crisis we saw liquidity in the stock market drying up as a precursor to the crisis in the real economy. We show that such effects are not new; in fact, we find a strong relation between stock market liquidity and the business cycle. We also show that investors' portfolio compositions change with the business cycle and that investor participation is related to market liquidity. This suggests that systematic liquidity variation is related to a “flight to quality” during economic downturns. Overall, our results provide a new explanation for the observed commonality in liquidity.