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Tag: Financial Markets

  • SmartCoder-R1 improves secure smart contract generation

    What the study found

    The study reports that SmartCoder-R1, a model for generating smart contracts, achieved new best results among the compared systems. It produced code and reasoning that were rated highly for functionality, security, and clarity.

    Why the authors say this matters

    The authors say this matters because smart contracts manage high-value assets, and vulnerabilities can cause large financial losses. The study suggests that making LLM-generated smart contracts both more secure and more explainable could address two linked problems: hidden reasoning and insecure code.

    What the researchers tested

    The researchers built SmartCoder-R1 on Qwen2.5-Coder-7B, a large language model for code generation. They used continual pre-training on smart contract code, long chain-of-thought supervised fine-tuning on 7,998 expert-validated reasoning-and-code samples, and a reinforcement learning stage called Security-Aware Group Relative Policy Optimization on 1,691 samples.

    What worked and what didn't

    Against 18 state-of-the-art baselines on 756 real-world functions from 289 deployed contracts, SmartCoder-R1 achieved the top scores on five metrics: ComPass, VulRate, SafeAval, FuncRate, and FullRate. The abstract states that FullRate reached 50.53% and was 45.79% higher than the strongest baseline, DeepSeek-R1; it also reports human evaluation ratings of 82.7% for functionality, 85.3% for security, and 90.7% for clarity. The abstract does not report which specific cases remained difficult beyond the fact that performance was not perfect.

    What to keep in mind

    The summary provided does not describe external replication, deployment, or testing beyond the stated benchmark and human evaluation. The abstract also does not give details on the validation criteria beyond compilability, functionality, security, and reasoning completeness.

    • SmartCoder-R1 is presented as a secure and explainable smart contract generation framework.
    • The model uses continual pre-training, supervised fine-tuning, and reinforcement learning.
    • The study reports top performance on five benchmark metrics across 18 baselines.
    • FullRate was 50.53%, which the abstract says is a 45.79% relative improvement over DeepSeek-R1.
    • Human evaluations rated the generated reasoning highly for functionality, security, and clarity.

  • Natural disaster uncertainty links differently with crypto, DeFi, and NFTs

    What the study found

    The study found that an innovative natural disasters index had different levels of connectedness with major cryptocurrencies, DeFi assets, and NFTs during the Russia-Ukraine conflict and elevated inflation. It also found that this uncertainty had a larger footprint on DeFi assets in bear markets and was more influential on NFTs in bull markets.

    Why the authors say this matters

    The authors conclude that the findings offer insights into how modern cryptocurrencies may survive during crises when conventional currencies devaluate. They also say the study can serve as a compass for monetary authorities and investors.

    What the researchers tested

    The researchers examined dynamic connectedness between an innovative natural disasters index and major cryptocurrencies, decentralized finance assets, and non-fungible tokens. They used data from 14 December 2021 to 31 January 2025 and applied three Quantile Vector Autoregressive (Q-VAR) specifications at lower, middle, and upper quantiles.

    What worked and what didn't

    Natural disaster uncertainty was reported to have a larger effect on DeFi assets in bear markets and a stronger influence on NFTs in bull markets. The Ripple, Synthetic, and Gala assets were described as the most tightly linked with natural disasters’ sentiment. The abstract also states that higher geopolitical and monetary uncertainties fuel a switch in investors’ decision-making criteria.

    What to keep in mind

    The summary only reports results from the abstract, so detailed limitations are not described. The findings are limited to the stated period, the Russia-Ukraine conflict context, and the Q-VAR specifications used in the study.

    • Natural disaster uncertainty showed different connectedness across cryptocurrencies, DeFi assets, and NFTs.
    • DeFi assets were more affected in bear markets, while NFTs were more influenced in bull markets.
    • Ripple, Synthetic, and Gala were the assets most tightly linked with natural disasters’ sentiment.
    • The authors say the findings may help explain how cryptocurrencies could behave during crises.

  • Speculative bubbles found in most examined DeFi and NFT assets

    What the study found

    The study found evidence of speculative price bubbles in most of the examined decentralized finance (DeFi) and non-fungible token (NFT) assets. Internet Computer was the only asset in the sample for which the bubble evidence was not found.

    Why the authors say this matters

    The authors say these bubbles may serve as early warning signals for investors. The study suggests this could be relevant because the assets are described as highly volatile and risky, while also offering profit opportunities.

    What the researchers tested

    The researchers analyzed selected DeFi and NFT assets: Internet Computer, Render, The Sandbox, Axie Infinity, Decentraland, Illuvium, Floki, Enjin Coin, and Vulcan Forged. They used the generalized sup augmented Dickey-Fuller test, a statistical test for identifying price bubbles.

    What worked and what didn't

    The GSADF test indicated price bubbles in all examined assets except Internet Computer. In other words, the test found bubble evidence for Render, The Sandbox, Axie Infinity, Decentraland, Illuvium, Floki, Enjin Coin, and Vulcan Forged, but not for Internet Computer.

    What to keep in mind

    The abstract does not describe the study period, data frequency, or other design details. The findings are limited to the specific assets named in the abstract and to the test used in the study.

    • The study examined speculative bubbles in selected DeFi and NFT assets.
    • The GSADF test found price bubbles in all examined assets except Internet Computer.
    • The authors describe these bubbles as possible early warning signals for investors.
    • The abstract says the assets are highly volatile and involve substantial risk.
    • The summary does not provide the study period or other design details.

  • Energy prices and fertilizer costs affect agricultural prices

    What the study found

    The study found that agricultural prices are affected both directly by energy prices and indirectly through fertilizer prices. The authors also report that nonlinear Fourier trends were used to remove a unit root problem, which is a statistical issue that can make time-series data harder to analyze.

    Why the authors say this matters

    The authors suggest that the links between energy, fertilizer, and agricultural markets matter because they show how price changes in one market can affect the others. The findings indicate that these relationships should be considered when examining agricultural price behavior.

    What the researchers tested

    The researchers studied monthly data from January 1990 to February 2021, and extended the analysis up to 2024. They examined the direct impact of crude oil and natural gas prices on wheat, maize, and soybean markets, as well as the indirect effect through fertilizer prices, using a Fourier-based time-series analysis.

    What worked and what didn't

    The conditional covariances of energy and fertilizer prices captured the spikes during the 2008 crisis and the volatile period afterward. The deterministic nonlinear Fourier trends eliminated the unit root problem, and the results showed both direct and indirect effects on agricultural prices.

    What to keep in mind

    The abstract does not provide detailed limitations, effect sizes, or statistical significance levels. It also does not describe the full set of results for the extension through 2024 beyond noting that the analysis was extended to that year.

    • Agricultural prices were affected directly by energy prices.
    • Agricultural prices were also affected indirectly through fertilizer prices.
    • The study used monthly data from January 1990 to February 2021, with analysis extended to 2024.
    • The conditional covariances of energy and fertilizer prices captured spikes during the 2008 crisis and the volatile period afterward.
    • Nonlinear Fourier trends were used to address a unit root problem.

  • Sports tokens show spillover risk and portfolio benefits

    What the study found

    The study found that sports tokens and sports equities are connected to risk spillovers with traditional assets. It also found that the minimum connectedness portfolio suggests possible portfolio benefits from using sports tokens.

    Why the authors say this matters

    The authors conclude that the findings provide information for market participants to manage asset risk. The study suggests that understanding these spillovers may help in curbing asset management risk.

    What the researchers tested

    The researchers examined risk spillover across sports tokens, sports equities, and other traditional assets using a quantile-VAR model, which is a model for analyzing relationships across different parts of the return distribution. They also used a minimum connectedness portfolio analysis.

    What worked and what didn't

    In the static analysis, token BAR was reported to cause significant shocks to the system. At the lower quantile, the USD was both the highest contributor and the highest receiver in the network, and OG was described as the dominant contributor. The portfolio analysis suggested possible benefits from using sports tokens.

    What to keep in mind

    The abstract does not describe detailed data, sample size, time period, or asset list beyond the examples named. It also does not provide full numerical estimates or specify which particular portfolio settings produced the reported benefits.

    • The study examined risk spillovers across sports tokens, sports equities, and traditional assets.
    • BAR was reported to cause significant shocks in the static analysis.
    • At the lower quantile, the USD was the highest contributor and the highest receiver in the network.
    • OG was described as the dominant contributor to the system.
    • The minimum connectedness portfolio suggested possible portfolio benefits from sports tokens.

  • Stock splits and reverse splits affected returns in Indonesia

    What the study found

    The study found that stock splits were generally associated with positive stock returns in the Indonesia Stock Exchange during 2022-2023, while reverse stock splits were associated with negative returns. In the abstract, these patterns are described through changes in cumulative abnormal return, or CAR, which compares actual returns with expected returns.

    Why the authors say this matters

    The authors conclude that stock splits are viewed as positive signals about a company’s future performance, while reverse stock splits are often interpreted as signs of possible financial or operational problems. The study also says this may help managers, investors, and regulators make more informed decisions in dynamic capital markets such as Indonesia.

    What the researchers tested

    The researchers examined the impact of stock splits and reverse stock splits on stock returns around their effective dates in the Indonesia Stock Exchange. They used an event study method and a cumulative abnormal return, or CAR, approach over a 10-day window before and after the corporate action.

    What worked and what didn't

    For stock splits, CAR increased significantly before the effective date, which the abstract describes as a favorable investor reaction. For reverse stock splits, CAR declined sharply on and after the effective date, indicating a negative effect on stock returns.

    What to keep in mind

    The abstract limits the study to the Indonesia Stock Exchange and the 2022-2023 period. It does not provide additional limitations beyond this scope in the available summary.

    • Stock splits were generally associated with positive stock returns in the Indonesia Stock Exchange.
    • Reverse stock splits were associated with negative stock returns.
    • CAR, or cumulative abnormal return, was used to compare actual and expected returns.
    • The study used a 10-day window before and after each corporate action.
    • The authors say stock splits may signal better future performance, while reverse splits may signal problems.

  • FinTech, AI, and Blockchain are linked to higher G20 sustainability performance

    What the study found

    The study found that FinTech adoption, AI readiness, and Blockchain activity were each positively and statistically significantly linked to sustainable development performance across G20 economies. It also found that using these technologies together was associated with larger sustainability gains.

    Why the authors say this matters

    The authors conclude that digital transformation functions as a strategic driver of sustainability. They also say the findings offer policy-relevant insights for G20 governments seeking inclusive, transparent, and environmentally responsible development.

    What the researchers tested

    The researchers examined G20 economies from 2015 to 2023 using cross-country panel data and macroeconomic controls. They tested the direct and complementary effects of FinTech adoption, AI readiness, and Blockchain activity on Sustainable Development Goal, or SDG, outcomes, drawing on Innovation-Driven Growth Theory, the Technology–Organization–Environment framework, and Institutional Theory.

    What worked and what didn't

    FinTech, AI, and Blockchain each showed a positive and statistically significant relationship with national sustainability performance. AI had the strongest effect, and the analysis also indicated meaningful digital complementarities, meaning coordinated adoption was associated with greater gains. The abstract does not report any technology that failed to show an effect.

    What to keep in mind

    The abstract describes results for G20 economies only, over 2015–2023. It also does not provide detailed limitations beyond the scope of the data and variables described.

    • FinTech adoption, AI readiness, and Blockchain activity were each positively linked to sustainability performance in G20 economies.
    • AI showed the strongest association with sustainable development outcomes.
    • Coordinated adoption of the technologies was associated with larger sustainability gains.
    • The study used cross-country panel data from 2015 to 2023.
    • The abstract does not describe a technology that had no effect.

  • FinTech and board traits are linked to higher cash holdings in Jordanian banks

    What the study found

    The study found a significant positive association between FinTech and board characteristics and banks’ cash holdings in Jordanian banks. In the abstract, the authors also say these findings support consumer theory and agency theory.

    Why the authors say this matters

    The authors conclude that the findings may help stakeholders, executives, bankers, customers, and policymakers understand the relationship between FinTech, board characteristics, and banks’ cash holdings in Jordan. They also suggest that banks in emerging economies should offer green loans for sustainability projects by leveraging FinTech adoption and board directors’ expertise to reallocate excess cash holdings.

    What the researchers tested

    The researchers examined the association between FinTech, board characteristics, and banks’ cash holdings using data from 14 banks listed on the Amman Stock Exchange from 2009 to 2024. They used panel data econometric analysis with ordinary least squares (OLS) as the main approach, and also applied two-stage least squares (2SLS), generalized method of moments (GMM), a dynamic model, and fixed effects regression to address causality and endogeneity issues.

    What worked and what didn't

    The analyses indicated that FinTech and board characteristics were significantly and positively connected with banks’ cash holdings. The abstract does not report any null or negative findings.

    What to keep in mind

    The study is based on 14 Jordanian banks, so its scope is limited to that setting and time period. The abstract does not describe specific limitations beyond noting the need for future work in other Middle Eastern countries.

    • FinTech and board characteristics were positively associated with banks’ cash holdings.
    • The study used panel data from 14 banks listed on the Amman Stock Exchange from 2009 to 2024.
    • OLS was the main analysis, with 2SLS, GMM, a dynamic model, and fixed effects regression used as additional checks.
    • The authors say the findings support consumer theory and agency theory.
    • The abstract suggests future research in other Middle Eastern countries, especially Jordan-focused work.

  • SOI spillovers differ across grain futures markets

    What the study found

    The study found that Southern Oscillation Index (SOI) signals, which track El Niño and La Niña climate conditions, are linked to grain futures returns and volatility, with the strongest effects in SAFEX maize. The authors report that these climate-linked effects vary across time horizons and frequency bands.

    Why the authors say this matters

    The authors conclude that SOI-based signals may provide early warning signals for grain market hedging. They also say the findings highlight regional differences in climate vulnerability.

    What the researchers tested

    The researchers studied time-frequency transmission between SOI and grain futures using CBOT corn, CBOT soybeans, and SAFEX maize data. They used partial wavelet coherence, multi-scale wavelet decomposition, Granger causality, and wavelet quantile regression, while controlling for macro-financial confounders.

    What worked and what didn't

    The results show pronounced scale-dependent SOI spillovers in SAFEX maize, with co-movements intensifying at medium- to long-term horizons. Both 30-day and 90-day SOI aggregates had statistically significant forecasting power for SAFEX maize returns and volatility, while CBOT corn and soybean futures showed weaker and more episodic sensitivity.

    What to keep in mind

    The abstract does not provide detailed limitations beyond the study scope. The findings are limited to the grain futures markets and methods described in the summary.

    • SOI signals were linked to grain futures returns and volatility.
    • SAFEX maize showed the strongest and most consistent spillovers.
    • 30-day and 90-day SOI averages significantly forecast SAFEX maize returns and volatility.
    • CBOT corn and soybean futures were less sensitive and more episodic.
    • The authors say SOI signals may help with grain market hedging.

  • Flexible central bank frameworks may curb inflation without major growth losses

    What the study found

    The study finds that central banks can maintain price stability without significantly undermining economic growth when they use flexible policy frameworks. It highlights inflation expectations, macro-financial linkages, and credibility mechanisms as part of this approach.

    Why the authors say this matters

    The authors conclude that these findings matter because they show a way to balance inflation control and growth. The study suggests that central banks may be able to use policy tools without forcing a large trade-off between the two goals.

    What the researchers tested

    The paper synthesizes theoretical frameworks, policy instruments, and empirical evidence from advanced and emerging economies. It examines central bank strategies such as interest rate policies, inflation targeting, macroprudential regulation, and forward guidance.

    What worked and what didn't

    The findings suggest that flexible frameworks can help central banks stabilize prices while supporting growth. The abstract also identifies trade-offs in using different tools, but it does not give a detailed breakdown of which specific instruments worked best or worst in each setting.

    What to keep in mind

    The available summary does not provide detailed study design, data sources, or quantitative results. It also does not describe specific limitations beyond noting that the analysis draws on both advanced and emerging economies.

    • The paper says central banks can curb inflation without significantly harming growth.
    • It focuses on flexible frameworks that include inflation expectations and credibility mechanisms.
    • The study reviews interest rate policy, inflation targeting, macroprudential regulation, and forward guidance.
    • The analysis combines theory, policy tools, and empirical evidence from advanced and emerging economies.
    • The abstract notes trade-offs, but it does not specify detailed comparative results.