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on Financial Markets |
| By: | Robert Novy-Marx; Mamdouh Medhat |
| Abstract: | A lot! Profitability subsumes all of “quality” investing, explaining both the performance of the strategies that industry markets and the factors that academics employ. It also has striking power pricing “defensive equity” strategies that overweight low-beta or low-volatility stocks. Profitability tilts explain all the abnormal performance of popular “alternative value” strategies, including those adjusted for “intangibles, ” and half of value's post-2007 underperformance. Profitability is crucial for pricing a wide array of seemingly unrelated anomalies, yielding a more parsimonious understanding of the cross section of expected returns. |
| JEL: | G10 G12 |
| Date: | 2025–03 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:33601 |
| By: | Abhishek Bhardwaj; Abhinav Gupta; Sabrina T. Howell; Kyle Zimmerschied |
| Abstract: | Do returns in private equity (PE) rise or fall with fund scale? This question is increasingly urgent amid larger funds and new focus on the retail market. Since better managers can raise larger funds, the causal effect is difficult to identify. We develop an instrument based on gifts to universities, which lead to more capital for managers with preexisting relationships. We show decreasing returns; for example, a 1% size increase reduces net IRR by 0.1 percentage points. Larger funds do larger deals, which perform worse. We find no change in risk, in part because additional deals are more levered. |
| JEL: | G11 G23 G24 |
| Date: | 2025–03 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:33596 |
| By: | Yu Zhang; Zelin Wu; Claudio Tessone |
| Abstract: | Cryptocurrencies are digital tokens built on blockchain technology, with thousands actively traded on centralized exchanges (CEXs). Unlike stocks, which are backed by real businesses, cryptocurrencies are recognized as a distinct class of assets by researchers. How do investors treat this new category of asset in trading? Are they similar to stocks as an investment tool for investors? We answer these questions by investigating cryptocurrencies' and stocks' price time series which can reflect investors' attitudes towards the targeted assets. Concretely, we use different machine learning models to classify cryptocurrencies' and stocks' price time series in the same period and get an extremely high accuracy rate, which reflects that cryptocurrency investors behave differently in trading from stock investors. We then extract features from these price time series to explain the price pattern difference, including mean, variance, maximum, minimum, kurtosis, skewness, and first to third-order autocorrelation, etc., and then use machine learning methods including logistic regression (LR), random forest (RF), support vector machine (SVM), etc. for classification. The classification results show that these extracted features can help to explain the price time series pattern difference between cryptocurrencies and stocks. |
| Date: | 2025–04 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2504.12771 |
| By: | Vasilios Plakandaras (Department of Economics, Democritus University of Thrace, Komotini, Greece); Matteo Bonato (Department of Economics and Econometrics, University of Johannesburg, Auckland Park, South Africa; IPAG Business School, 184 Boulevard Saint-Germain, 75006 Paris, France); Rangan Gupta (Department of Economics, University of Pretoria, Private Bag X20, Hatfield 0028, South Africa); Oguzhan Cepni (Ostim Technical University, Ankara, Turkiye; University of Edinburgh Business School, Centre for Business, Climate Change, and Sustainability; Department of Economics, Copenhagen Business School, Denmark) |
| Abstract: | This paper forecasts monthly cross-sectional realized variance (RV) for U.S. equities across 49 industries and all 50 states. We exploit information in both own-market and cross-market (oil) realized moments (semi-variance, leverage, skewness, kurtosis, and upside and downside tail risk) as predictors. To accommodate cross-sectional dependence, we compare standard econometric panel models with machine-learning approaches and introduce a new machine-learning technique tailored specifically to panel data. Using observations from April 1994 through April 2023, the panel-dedicated machine-learning model consistently outperforms all other methods, while oil-related moments add little incremental predictive power beyond own-market moments. Short-horizon forecasts successfully capture immediate shocks, whereas longer-horizon forecasts reflect broader structural economic changes. These results carry important implications for portfolio allocation and risk management. |
| Keywords: | Cross-sectional realized variance, Realized moments, Machine learning, Forecasting |
| JEL: | C33 C53 G10 G17 |
| Date: | 2025–04 |
| URL: | https://d.repec.org/n?u=RePEc:pre:wpaper:202518 |
| By: | Dirk Bezemer; Richard Senner |
| Abstract: | Strongly rising asset prices after March 2020 in the euro area and the United States are hard to explain using conventional asset pricing approaches. We apply Liquidity Preference Theory to examine the consequences of the unprecedented Covid-related growth of household deposits. Following the shock, deposits were spent, hoarded, or invested depending on liquidity preferences (Keynes, 1936; Tobin 1969). The allocation of the deposit shock was mediated by income distribution, corporate financial dynamics and the price inelastic response of financial and real estate asset markets, combined with the elasticity of the financial system (Borio and Disyatat 2011; Gabaix and Koijen 2022). Stability of liquidity preferences was evidenced by the fact that, just as before the pandemic, every additional Euro or Dollar in monetary wealth during the pandemic came to be reflected in around six Euros or ten Dollars in non-monetary wealth, mainly equities and housing. This suggests portfolio rebalancing is the major explanation of the strong rise in asset prices after the onset of the pandemic. |
| Keywords: | Asset pricing, Deposits, Household savings, Macrofinance, Liquidity |
| JEL: | E42 E51 G11 G22 G21 |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:snb:snbwpa:2025-05 |