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Keynes answered to critics of the General Theory, in 1937, that they failed to realize that there were two main innovations in that work. The first, was the relationship between money demand and uncertainty; the second was the consumption multiplier. The relation between money demand and uncertainty was in fact the main reason to explain why aggregate demand could fall short of full employment income. However, this was explained by Keynes in 1937 by recourse to a form of precautionary demand for money. In The GT, Keynes had actually merged the precautionary demand into the transactions demand for money, making it very difficult for any reader, friendly or unfriendly, to actually see what he meant in 1937. As a result, Keynes liquidity preference theory of the interest rate in the GT exhibited some important shortcomings that were the subject of many reexaminations, including one by Richard Kahn and another by James Tobin. The paper evaluates Keynes's views, Kahn's and Tobin's solutions to Keynes's dilemmas. At its conclusion it is shown why these themes remain relevant today, particularly when financial systems are in turmoil.

This article shows that the \"risk premium\" shock in Smets and Wouters (2007) can be interpreted as a structural shock to the demand for safe and liquid assets such as short-term U.S. Treasury securities. Several implications of this interpretation are discussed.

Extremes of the Edgeworth box concern corner allocations and their relationship to the contract curve in a two-good, two-agent exchange economy. In the standard pure-exchange setting with well-behaved preferences, the contract curve comprises all Pareto-efficient allocations, including interior tangencies and boundary corners, where no mutually beneficial trade remains. When money is introduced as a numéraire (a medium of exchange only), real feasibility and preferences are unchanged, so the contract curve remains the benchmark for efficiency. When money provides liquidity services (is valued for holding), agents may rationally abstain from trade even near interior tangencies; short-run outcomes can therefore include inaction at corners. This entry defines these objects, outlines the efficiency conditions at boundaries, and summarizes how monetary interpretations affect short-run behavior in general equilibrium and monetary economics. The Edgeworth geometry remains a real-exchange depiction; when we discuss money as a store of value, we use it as a short-run, reduced-form outside option that proxies intertemporal motives. This does not “fix” the box; it clarifies why no-trade at or near corners can be individually rational when liquidity is valued.

This paper studies whether stock returns' sensitivities to aggregate liquidity fluctuations and the pricing of liquidity risk vary over time. We find that liquidity betas vary across two distinct states: one with high liquidity betas and the other with low betas. The high liquidity-beta state is short lived and characterized by heavy trade, high volatility, and a wide cross-sectional dispersion in liquidity betas. It also delivers a disproportionately large liquidity risk premium, amounting to more than twice the value premium. Our results are consistent with a model of liquidity risk in which investors face uncertainty about their trading counterparties' preferences.

This paper investigates the interaction between fiscal policy and non-performing loans (NPLs), a nexus often overlooked in banking stability literature. By proposing a generalized theoretical framework that augments the industrial organization (IO) theory of banking with liquidity preference theory, this study explains why a fiscal contraction (an improvement in the primary balance from deficit toward surplus) can decrease NPLs in a bank’s portfolio. Using bank-level quarterly data from Guyana (2009: Q4 to 2024: Q4) and a Panel Autoregressive Distributed Lag Pooled Mean Group (ARDL-PMG) model, we find that a fiscal contraction reduces NPLs in the long run. Specifically, a one-percentage-point improvement in the seasonally adjusted primary balance (as a % of GDP) is associated with a 0.473 percentage point decrease in NPLs in the long run. This finding contrasts with the existing literature, which often suggests that fiscal consolidations increase credit risk. In the short run, however, the results indicate a divergent effect where fiscal contractions lead to a temporary increase in NPLs, with a coefficient of 0.103, likely because of immediate pressure on borrower debt-service capacity. This study contributes to the literature by extending the IO theory of banking to the fiscal policy–NPL relationship in a developing, resource-rich economy. Notably, while higher oil prices and bank efficiency significantly lower NPLs, traditional macroeconomic drivers such as GDP growth, inflation, and the real effective exchange rate—as well as the COVID-19 pandemic—are found to be statistically insignificant in this framework.

This paper revisits Keynes’s theory of liquidity preference to emphasise its reliance on liquidity. By clarifying the meaning of ‘liquidity’ in the context of the theory, it is argued that liquidity preference is not based on the demand for money, the most tradable asset, or a theory of bearishness. Instead, liquidity preference represents a demand for price-protected (capital-safe) assets, most directly inside and outside money, but also cash-equivalent quasi-money such as self-liquidating assets and security repurchase agreements (repo). The theory of liquidity preference explains that the public is willing to forgo interest income to hold short-term price-protected assets due to the capital and price uncertainty associated with relying on market liquidity, or how easy it is to convert an asset into money. It follows that the rate of interest is a monetary phenomenon and is determined independently of saving and investment.

Financial contagion is modeled as an equilibrium phenomenon. Because liquidity preference shocks are imperfectly correlated across regions, banks hold interregional claims on other banks to provide insurance against liquidity preference shocks. When there is no aggregate uncertainty, the first‐best allocation of risk sharing can be achieved. However, this arrangement is financially fragile. A small liquidity preference shock in one region can spread by contagion throughout the economy. The possibility of contagion depends strongly on the completeness of the structure of interregional claims. Complete claims structures are shown to be more robust than incomplete structures.

In the context of digitalization, this study investigated the financing mechanisms and preferences of technology-driven small and medium-sized enterprises (TDSMEs) listed on the National Equities Exchange and Quotations (NEEQ) in China. Its primary objective was to identify the factors influencing financing decisions and to elucidate how TDSMEs choose their financing options in a rapidly evolving digital environment. To achieve this goal, we constructed a panel regression model using financial data from 41 TDSMEs (2017–2023), identifying the key determinants of financing decisions while examining the impact of regional heterogeneity and validating the model’s robustness. The empirical findings indicated that various independent variables, including a firm’s capital structure, significantly influenced both internal and external financing. Additionally, six machine learning (ML) algorithms were employed to predict financing preferences. Among them, the random forest (RF) model achieved the best financing preferences performance, with an average F1 score of 0.814, indicating its robust predictive capability for TDSMEs’ financing preferences. To further validate the proposed models, we conducted a case study on a TDSME newly recognized in 2024 (named TS Pharmaceutical). Both the Lasso and RF models demonstrated outstanding predictive accuracy, confirming the practicality of the ML models. These results provide valuable insights into navigating the ever-changing digital financing landscape, offering recommendations for policymakers and financial institutions to better support TDSMEs. The key innovation of this study lies in its novel integration of conventional panel regression analysis and ML techniques, thereby bridging the gap between digital transformation and financing strategies while contributing both theoretically and practically to the field.

Townshend wrote Keynes a letter on November 25th,1938 and asked Keynes the following question, after informing Keynes that he had read Keynes’s A Treatise on Probability a number of years earlier and understood Keynes’s concept of non-numerical probability: “This is the nearest I can get to an analysis of the part played by the factor of confidence in the rationale of interest. I believe that its further logical analysis at a deeper level of generalization is connected with the part played by the weight of evidence in your theory of probability, but I cannot see just how….” (Townshend 1979, 292; italics added). Townshend ‘s question can be rewritten in the following fashion: “Where, in your A Treatise on Probability, is your analysis of the connection between the variable, confidence, in the General Theory and the weight of evidence in your A Treatise on Probability, applied to support your analysis in the General Theory of your liquidity preference theory of the rate of interest?” Keynes’s response was direct and straightforward: “As regards my remarks in my General Theory, have you taken account of what I say on page 240, as well as what I say at page 148, which is the passage I think you previously quoted…”.(Letter to Townshend, Dec. 7th ,1938). The answer given here by Keynes is for Townshend to read p.240 of the General Theory; however, it relates directly to Keynes’s chapter 26 of the A Treatise on Probability. What Keynes provides the reader of the General Theory on p.240 is his statement that there is no discussion of how to estimate/calculate the risk and liquidity premiums in the General Theory. This paper demonstrated that Keynes’s discussion of how to estimate the risk and liquidity premiums occurs in chapter XXVI of the TP. This paper demonstrates the logical and mathematical links between Keynes’s General Theory liquidity preference theory of the rate of interest, where liquidity preference is defined as a function of uncertainty, U, and Keynes’s A Treatise on Probability analysis of the evidential weight of the argument, V, which equals w, which is expressed in degrees. Keynes is pointing out to Townshend that U is a function of V, which is equal to w expressed in degrees. Thus, U is a function of w. We can write this out in English-  The evidential weight of the argument, V, is equal to the degree of the completeness of the amount of information on which the probability is based. Thus, Uncertainty is a function of the evidential weight of the argument, while liquidity preference is a function of uncertainty. The analysis that is missing in all past discussions by economists and philosophers of this issue has been their failure to identify the role played by Keynes’s mathematical variable, w.

This paper aims at investigating to what extent there is a differentiated regional banking strategy in the Brazilian economy. Based on the Post Keynesian theory of regional liquidity preference (Dow, 1993), the paper analyzes consolidated balance sheets of bank branches located in different Brazilian regions. Through the analysis of indicators built using this database, the paper finds evidence to support the thesis that the Brazilian banking system's strategies are heterogeneous across the territory. Furthermore, we conclude that this behavior reinforces existing uneven regional patterns of development of the economy.