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1. Inflation and Asset Returns

   https://doi.org/10.1146/annurev-financial-110921-104726  

   Cieslak, Anna; Pflueger, Carolin (November 2023, Annual Review of Financial Economics)

   The past half-century has seen major shifts in inflation expectations, how inflation comoves with the business cycle, and how stocks comove with Treasury bonds. Against this backdrop, we review the economic channels and empirical evidence on how inflation is priced in financial markets. Not all inflation episodes are created equal. Using a New Keynesian model, we show how “good” inflation can be linked to demand shocks and “bad” inflation to cost-push shocks driving the economy. We then discuss asset pricing implications of “good” and “bad” inflation. We conclude by providing an outlook for inflation risk premia in the world of newly rising inflation. 

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2. The Darwinian Returns to Scale

   https://doi.org/10.1093/restud/rdad061  

   Baqaee, David Rezza; Farhi, Emmanuel; Sangani, Kunal (June 2023, Review of Economic Studies)

   Abstract How does an increase in market size, say due to globalization, affect welfare? We study this question using a model with monopolistic competition, heterogeneous markups, and fixed costs. We characterize changes in welfare and decompose changes in allocative efficiency into three different effects: (1) reallocations across firms with heterogeneous price elasticities due to intensifying competition, (2) reallocations due to the exit of marginally profitable firms, and (3) reallocations due to changes in firms’ markups. Whereas the second and third effects have ambiguous implications for welfare, the first effect, which we call the Darwinian effect, always increases welfare regardless of the shape of demand curves. We nonparametrically calibrate demand curves with data from Belgian manufacturing firms and quantify our results. We find that mild increasing returns at the microlevel can catalyze large increasing returns at the macrolevel. Between 70 and 90% of increasing returns to scale come from improvements in how a larger market allocates resources. The lion’s share of these gains are due to the Darwinian effect, which increases the aggregate markup and concentrates sales and employment in high-markup firms. This has implications for policy: an entry subsidy, which harnesses Darwinian reallocations, can improve welfare even when there is more entry than in the first best. 

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3. Predicting Returns with Text Data

   https://doi.org/10.2139/ssrn.3389884  

   Ke, Zheng; Kelly, Bryan T.; Xiu, Dacheng (May 2019, SSRN Electronic Journal)

   null (Ed.)

   We introduce a new text-mining methodology that extracts information from news articles to predict asset returns. Unlike more common sentiment scores used for stock return prediction (e.g., those sold by commercial vendors or built with dictionary-based methods), our supervised learning framework constructs a score that is specifically adapted to the problem of return prediction. Our method proceeds in three steps: 1) isolating a list of terms via predictive screening, 2) assigning prediction weights to these words via topic modeling, and 3) aggregating terms into an article-level predictive score via penalized likelihood. We derive theoretical guarantees on the accuracy of estimates from our model with minimal assumptions. In our empirical analysis, we study one of the most actively monitored streams of news articles in the financial system--the Dow Jones Newswires--and show that our supervised text model excels at extracting return-predictive signals in this context. Information in newswires is assimilated into prices with an ineffcient delay that is broadly consistent with limits-to-arbitrage (i.e., more severe for smaller and more volatile firms) yet can be exploited in a real-time trading strategy with reasonable turnover and net of transaction costs. 

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4. Reconciling λ-Returns with Experience Replay

   Daley, Brett C; Amato, Christopher (December 2019, Advances in neural information processing systems)

   Modern deep reinforcement learning methods have departed from the incremental learning required for eligibility traces, rendering the implementation of the λ-return difficult in this context. In particular, off-policy methods that utilize experience replay remain problematic because their random sampling of minibatches is not conducive to the efficient calculation of λ-returns. Yet replay-based methods are often the most sample efficient, and incorporating λ-returns into them is a viable way to achieve new state-of-the-art performance. Towards this, we propose the first method to enable practical use of λ-returns in arbitrary replay-based methods without relying on other forms of decorrelation such as asynchronous gradient updates. By promoting short sequences of past transitions into a small cache within the replay memory, adjacent λ-returns can be efficiently precomputed by sharing Q-values. Computation is not wasted on experiences that are never sampled, and stored λ-returns behave as stable temporal-difference (TD) targets that replace the target network. Additionally, our method grants the unique ability to observe TD errors prior to sampling; for the first time, transitions can be prioritized by their true significance rather than by a proxy to it. Furthermore, we propose the novel use of the TD error to dynamically select λ-values that facilitate faster learning. We show that these innovations can enhance the performance of DQN when playing Atari 2600 games, even under partial observability. While our work specifically focuses on λ-returns, these ideas are applicable to any multi-step return estimator. 

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5. Test for market timing using daily returns

   https://doi.org/10.1080/07350015.2021.2006670  

   Jiang, L.; Liu, W.; Peng, L. (January 2023, Journal of business economic statistics)