Creating more efficient trading platforms can lower costs but also distort trade patterns. We model brokers’ tokenization decisions—i.e., whether to represent assets as tradable tokens on a shared, programmable ledger. Brokers with heterogeneous market power compete to attract investors and execute their trades intra-broker or over-the-counter. Moreover, coalitions of brokers can invest in creating a tokenized market with faster, cheaper inter-broker settlement. Due to trade diversion incentives, equilibrium coalition structures feature excessive investment or insufficient unification. Public-private cost-sharing or interoperability mandates fail to achieve first best in isolation, but succeed when implemented jointly. These results withstand incorporating an open-access ledger (e.g., a public blockchain).

In this paper, we examine the influence of monetary policy on nineteen clean energy stock indices for the period 2010-2023. We uncover the common factors among various sectors and sub-sectors of clean energy stocks and shed light on how monetary policy shocks propagate across different sectors. Our findings reveal that the panel of indices is influenced mainly by one common factor that accounts for up to 60% of the data, and this factor responds positively to monetary policy shocks. However, the responses of individual clean energy stock indices vary remarkably across different sectors and sub-sectors. Some sectors, such as energy storage, green IT, and wind stocks, respond positively, while others, like smart grid, green building, and transportation stocks, show negative responses to monetary tightening. Understanding this heterogeneity is crucial for effective investment strategies and underscores the need for targeted policy interventions considering the sensitivity of individual sectors.

A long tradition of macroeconomic analysis accords monetary policy only a transient role

in driving real outcomes. At the same time, a large body of evidence highlights the long-lasting impact of boom-bust cycles. We present a model where monetary policy, through

its impact on and reaction to the financial cycle, influences long-term economic trajectories. The core setup is an overlapping generations model featuring bank financing – the creation of bank loans and inside money – which is critical for production and consumption. Monetary policy attains the first-best allocation by sustaining an efficient flow of financing. We then introduce coordination-failure frictions among lenders, which give rise to an endogenous boom-bust cycle in bank financing and an intertemporal policy tradeoff. A forward-looking policymaker optimally leans against excessive risk-taking during the boom, trading off short-term activity with longer-term stability. An inordinate focus on short-term outcomes can lead to ‘monetary policy hysteresis’, where low interest rates increase the vulnerability to financial busts over successive cycles. As a result, low rates can beget lower rates.

The relative restrictiveness of a central bank’s supply of money predicts the raw and risk-adjusted returns of its currency—both next month and at least three years into the future. Ar-chived data, known by currency traders at the time, estimates central bank restrictiveness as a scaling of the residual from out-of-sample panel regressions of M1 on macroeconomic variables tied to domestic and international transaction requirements. Carry’s ability to forecast currency returns is subsumed by the central bank restrictiveness signal, which also forecasts inflation.

This paper examines if equity funds’ investment in environment, social, and governance (ESG) affects their capital flows and performance. Utilizing a novel fund-level ESG metric, we find that fund-portfolio-level ESG negatively attracts money inflows, this effect is more pronounced for unsophisticated investors. Also, stocks with high ESG scores tend to underperform, while funds with more ESG investment do not generate inferior performance. It suggests that fund managers process active skills to cover the cost of ESG investment and may find the optimal level of it in their portfolios. Further tests show that the skills of fund managers with more ESG investment may be attributed to their experienced ESG information in stock investment. Our results also provide new insights into the mechanisms behind investing in socially responsible funds.

This study presents a method to analyse social media interactions and their relationship with investor decisions and stock market movements. It explores three questions: (1) the best method for classifying stock-related social media posts, (2) the influence of posts on stock returns and trading volumes, and (3) the impact of posts by opinion leaders. WallstreetBets board in Reddit social media and the GameStop stock provide an ideal laboratory for answering these questions. We analysed 135,000 posts, 5.6 million comments, and 90,000 users from November 2020 to June 2021. Using natural language processing and deep learning, posts and comments were classified by user intention (buy, sell, hold). We found that a BERT-based classifier achieved the highest performance with an F1 score of 80.2%. We then analysed the relationship between social media activity and stock market data, finding that posts are related to trading volumes but not stock returns. However, considering the popularity of posts, there is a significant relationship between social media opinions and stock returns and trading volumes. The study highlights the importance of "influencers": posts from a few influential users significantly impact stock returns and volumes. This research underscores the importance of social media dynamics in financial market behaviour.

Does early-life disaster experience influence the way investors price financial assets? Using household registration information of bankers originating large corporate loans, we find unique evidence that bankers who had experienced higher number of major disaster events charge higher loan spreads than those experienced less or not. This effect holds regardless of bankers’ risk preference in the selection of borrowers. It becomes stronger for borrowing firms with lower profitability and leverage, and those with higher pre-existing lending relationship with individual bankers, and even those with higher climate risk exposure. Our results are not driven by bank, banker characteristics and borrower fundamentals, but suggest that investor conservatism, driven by early disaster experience, shapes assets price.

Using a large dataset of US firms over the period 2007–2022, I find that institutional ownership is associated with fewer biodiversity risk incidents, suggesting a significant role of institutional investors in combating biodiversity loss. The finding is robust to various fixed effects, extends to alternative measures of biodiversity risks, and persists even after adjusting for past incidents. Using plausibly exogenous variation in investors’ monitoring ability indicates causality. The effect is strongest for long-term and domestic institutional investors. Foreign institutions are generally associated with an increased risk of biodiversity, but this relationship is less pronounced when they originate from countries with a strong environmental and biodiversity awareness. Finally, biodiversity risk incidents are positively associated with the implied cost of equity capital, consistent with the notion that investors demand compensation for biodiversity risk exposure.