Past Issues
Volume 1, Issue 3
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According to a famous Wall Street saying, "as goes January, so goes the year." Popularly known as the January barometer, it claims that the stock market's direction in January predicts its direction for the rest of the year. This paper tests the predictive performance of the January barometer in the 21st century. Using Center for Research in Security Prices (CRSP) value-weighted U.S. market returns from 2001 through 2025, January returns are compared with February-December returns using descriptive statistics, chi-square test of independence, regression, and Welch's t-test, along with other robustness tests. The barometer correctly predicted the direction of the rest of the year in 14 of 25 years (56%). The market went up in 19 of the 25 years, so the barometer performed worse than simply predicting a rising market over the year. The chi-square test detects no relationship between January's direction and the rest-of-year direction (χ² = 0.12, p-value = 0.73). The OLS regression shows that January returns explain less than 1% of the variation in rest-of-year returns (slope coefficient = 0.23, p-value = 0.78). On average, February-December returns after up Januarys (11.3%) and down Januarys (9.2%) are also not significantly different (p-value = 0.80). Additional robustness tests also confirm that the January performance has not reliably forecasted the performance of the rest of the year in the 21st century.
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14 Oct 2026
Electronic waste is a growing environmental and public-health concern, creating demand for electronic systems that reduce persistent waste. Silk fibroin has received attention as a flexible, biodegradable, biocompatible, and processable biomaterial for electronic applications. In this conceptual feasibility study, we evaluate the potential use of silk fibroin within SilkBound, a proposed layered electronic system intended for temporary medical, wearable, and environmental applications. We combined findings from published research on silk-based and transient electronics with preliminary market evidence collected through a user survey. Technical feasibility was evaluated based on material function, electrical integration, flexibility, mechanical durability, moisture sensitivity, thermal stability, reliability, device lifespan, degradation control, and manufacturing requirements. The preliminary survey was used to identify possible adoption drivers and barriers and is reported with its methodological detail. Existing research supports the use of silk fibroin as a substrate, dielectric, encapsulation material, and, in some systems, a functional electronic component. However, the feasibility of SilkBound as a complete device cannot yet be confirmed because an integrated prototype has not been fabricated or tested. The concept appears most promising for specialized temporary applications in which flexibility and controlled degradation offer clear advantages over conventional materials. Future research should identify an initial application, select compatible materials, fabricate a prototype, and evaluate performance, degradation, safety, environmental impact, manufacturing scalability, and cost.
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The 2008 financial crisis exposed significant vulnerabilities arising from the interaction of cheap credit, excessive mortgage lending, securitisation, institutional incentives, leverage and financial interconnectedness. This paper examines whether policymakers learned the right lessons from the crisis by evaluating its underlying causes and the effectiveness of subsequent regulatory reforms. Drawing on academic literature, historical financial data and regulatory documents, the analysis explores how risks accumulated within the US housing market and spread throughout the wider financial system. It then evaluates how Basel III and the Dodd-Frank Act addressed weaknesses in capital adequacy, liquidity, leverage and regulatory oversight. Although these reforms aimed to strengthen financial institutions, systemic vulnerabilities remain, particularly as risks can migrate towards less regulated areas of finance. Building on Dani Rodrik's argument concerning excessive confidence in particular economic models and Nassim Nicholas Taleb's analysis of extreme financial events, the paper argues that the crisis exposed the limitations of relying on historical relationships and assumptions that may fail under changing conditions. While the available evidence cannot establish the independent causal effects of individual reforms, it highlights the importance of recognising uncertainty in financial modelling. The paper concludes that policymakers addressed many weaknesses revealed by the crisis, but the broader lesson remains incomplete: financial institutions must remain resilient when economic models and historical assumptions fail.
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This study examines how switching from traditional software licenses to a recurring subscription model impacts a company's financial performance and market valuation over time. For this comparative case study, this paper analyzes Adobe Inc. and Autodesk, Inc. by looking at their financial data from the pre-transition, active-transition and post-transition periods. The study focuses on the important financial metrics like total revenue, GAAP net income, operating margins, Annual Recurring Revenue (ARR), Enterprise Value-to-Sales (EV/Sales) multiples, Price-to-Earnings (P/E) multiples and stock price volatility. The findings show that both companies faced problems during their transitions, with temporary declines in metrics like revenue, profitability, and operating margins. On the other hand, ARR increased significantly as the subscription revenue became a larger part of each company's business. The valuation multiples generally increased and stock price volatility fluctuated over time. These results suggest that some of the decline in financial performance during a transition to a subscription model may be related to changes in when the revenue is actually recognized. However, this study does not measure how much of this decline can be attributed to these accounting changes. Overall, the analysis found that Adobe and Autodesk experienced temporary declines in their financial performance during the transition before recovering as recurring revenue increased. Both companies also experienced higher EV/Sales valuations in later years, but this does not prove that switching to a subscription model would produce similar results for other companies.
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This paper examines whether young investors who see investing as entertainment are more likely to report buying a meme stock or another investment trending on social media. I use the 2024 FINRA National Financial Capability Study Investor Survey, which includes 213 respondents ages 18 to 34. Among 197 respondents with valid motive and purchase responses, weighted purchase rates are 21.9 percent for not at all, 37.5 percent for somewhat, and 45.1 percent for very well. The primary weighted model includes 183 respondents and adjusts for objective investment knowledge, risk willingness, experience, and portfolio value. Entertainment odds ratios are 1.50 and 1.84, with a joint p-value of .386. The very-well estimate has a 95 percent confidence interval of 0.76 to 4.46, leaving H1 inconclusive. The survey cannot establish whether risk willingness precedes entertainment or partly reflects it. The analysis does not support the predicted protective interaction with knowledge or a clear entertainment association with frequent trading. The positive knowledge coefficient is exploratory and is not statistically significant in the alternative-score or unweighted checks. In the primary sample, the 58 purchasers averaged 4.31 correct answers out of ten, compared with 3.68 for the 125 non-purchasers, using WGT1 weights. Purchasers scored higher descriptively, but both groups averaged fewer than half the questions correct; this does not establish high knowledge.
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12 Oct 2026
This paper compares Pakistan's commercial banking sector and Germany's universal Hausbank model in order to determine why Pakistani small and medium enterprises suffer from credit starvation while large corporations secure adequate financing. Legal constraints, court recovery bottlenecks (judicial liquidation delays), and high fiscal borrowing force Pakistani commercial banks to rely on heavy physical collateral and low-risk sovereign securities such as Treasury bills rather than industrial lendings. The feasibility of implementing the Hausbank model in Pakistan is discussed by gathering data collected from qualitative interviews involving high-ranking banking officers and from secondary sources. It highlights why some senior bankers reject the Hausbank model, referring to opaque family-owned businesses that dominate Pakistan's corporate governance. Similarly, it explores how government debt causes the banks to shift their liquidity, removing financial incentives to extend credit to the private sector. Regardless, the paper discusses a third approach to tackle the risky, complex economic climate of Pakistan.
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Industry allocation is an important component of asset allocation and a major source of excess returns in active investing. This study investigates the predictability of sector index returns in China using a multifactor framework. Using monthly data from January 2019 to June 2026 for eleven CSI 800 first-level sector indices, we construct 11 factors including indicator (CPI, PPI, PMI, M2), interest rates (Shibor), technical indicators (momentum and volatility), and a market factor. We model each industry separately and estimate by using two linear models: ordinary least squares (OLS) and Lasso regularized regression, with the period from January 2025 to June 2026 as an out-of-sample test window. A one-shot training scheme (fixed sample of January 2019 to December 2024) yields negative out-of-sample R² for both models (−0.72 for OLS and −0.12 for Lasso), which was normal in monthly financial return prediction. To improve the forecasts, we adopt rolling window re-estimation: at each month's end, the models are refitted on the most recent 72, 60, and 48 months of data. Rolling-window estimation is associated with improved numerical accuracy and strategy returns: the 72-month rolling Lasso raises the average out-of-sample R² to −0.07 and delivers a Top-3 industry rotation strategy with a cumulative return of 65.45%, an excess return of 48.03 percentage points over the equal-weight benchmark; the 72-month rolling OLS earns 37.46% (excess +20.04%). Since both models produce negative R² for out-of-sample prediction, we further investigate their ranking power using Spearman rank information coefficient (Rank IC) calculated on a monthly cross-section basis: using the 72-month rolling window approach, the mean Rank IC equals +0.215 (t = 2.96) for Lasso, compared to just +0.057 (t = 0.60) for OLS. Thus, the ranking power of Lasso strategy provides support to the presence of excess returns, while the ranking power of OLS strategy is not statistically different from zero. These results suggest that rolling-window estimation can help models adapt to changing market conditions. Compared with OLS, Lasso shows stronger out-of-sample ranking performance, which is consistent with the excess returns generated by the corresponding industry rotation strategy.
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Environmental consultants are pivotal to our modern economy, playing a key role in supporting environmental protection and regulatory compliance (Lyndsey, 2026). However, maintaining financial sustainability remains challenging due to the complex cost structures of environmental consulting projects. This study examines the relationship between project costs, revenue, and financial performance using real-world financial data from an environmental consulting company. By analyzing six projects representing different environmental consulting services, the study evaluates cost distribution, profit margins, and operational efficiency through financial and comparative analysis. The analysis identifies four major findings. First, cost structures vary significantly across projects. Second, operational costs account for a high proportion of total costs in some projects, especially Project F and Project D, where they account for 82.63% and 65.67%, respectively. Third, high labor costs do not necessarily indicate inefficiency. Project E has a labor cost proportion of 55.72% but achieves the highest profit margin of 68.41%. Fourth, high operational expenses do not necessarily result in positive financial performance. Project F has an operational cost proportion of 82.63% and a profit margin of −17.12%, showing that high operational expenses combined with limited revenue can create financial pressure. Based on these findings, environmental consulting companies can improve financial sustainability through operational efficiency, project-specific budgeting, and appropriate professional investment while maintaining service quality and environmental objectives.
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Concert organizers must balance financial success, operational efficiency, and environmental sustainability, yet these factors are often evaluated separately. Artificial intelligence and workflow automation offer opportunities for integration, but integrated decision-support systems remain limited in concert planning. This study develops an AI-assisted concert-planning decision-support system implemented in n8n. It combines user-defined event parameters with external artist-popularity metrics and currency exchange services to estimate audience demand, environmental indicators, and financial feasibility, and generate structured planning recommendations within one workflow. A case study based on Coldplay's Music of the Spheres World Tour concert at Olympiastadion Berlin demonstrates the framework's functionality. The system predicted attendance of 72,306 spectators, approximately 97% of stated venue capacity, and generated planning estimates for energy consumption, carbon emissions, waste, transportation, ticket revenue, costs, and profitability. Because complete verified concert-specific data were unavailable, these outputs demonstrate system functionality and plausibility rather than validate predictive accuracy or reconstruct actual concert performance. The framework illustrates how integrated AI and workflow automation can support efficiency, transparency, and consistency in pre-event decision-making. It also shows how environmental sustainability and economic viability can be assessed together rather than as independent objectives. Intended to support early-stage feasibility assessment, the system does not replace detailed operational analyses or comprehensive life-cycle assessments. It provides a practical approach to more sustainable, transparent, and data-driven planning in the live event industry.
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08 Oct 2026
This paper examines how fitness industry practices may contribute to the risk of overtraining and influence exercise behavior among consumers. While overtraining is often viewed as the result of individual decision-making and personal discipline, this research argues that broader industry forces may also contribute to the issue. Using primary research from surveys as well as secondary research from industry reports, academic journals, fitness marketing materials, and health-related publications, the study explores the possible influences on exercise and recovery behavior. The findings suggest that fitness business models, marketing strategies, and performance-based incentives may encourage individuals to train more intensely and more frequently than is sustainable. Although awareness of overtraining has increased, many individuals may overtrain due to various societal pressures. How often one trains as well as their prioritization of recovery may be partly influenced by fitness industry structures that create training incentives and social pressures, and this paper explores whether this relationship warrants further study.
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07 Oct 2026
This paper aims to investigate the effect of instantaneous asset price effects for the four leading global equity indices, i.e., Nifty 50 (India), S&P 500 (US), FTSE 100 (UK) and Shanghai Composite (China) indices during the 2026 Iran-Israel-USA War, using the event study methodology. Using an event window of one day, the current research tries to uncover the asset price effects resulting from the announcements of the outbreak of war, intensification of the military action, and the ceasefires via the country level crude oil indices. It was found that there was asymmetry at the country level that resulted from the domestic reliance on energy and the composition of the sectors. The declared ceasefire on June 11 acted as the trigger of the market de-escalation mechanism, leading to coordinated relief rallies of all four indices. The market reaction to executive digital messages in real-time provides a good basis for testing the Semi-Strong Form of the Efficient Market Hypothesis in the presence of high geopolitical risks.
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06 Oct 2026
Buy Now, Pay Later (BNPL) installment credit has expanded rapidly among U.S. consumers: the six largest pay-in-four providers originated $43.9 billion in loans in 2023, up from $2.2 billion in 2019 (Consumer Financial Protection Bureau 2025a). Adoption concentrates among young adults: consumers aged 18 to 24 financed a purchase with BNPL at nearly twice the overall population's rate in 2022 and carry a disproportionate debt share (Consumer Financial Protection Bureau 2025b). This paper asks whether publicly available U.S. data reveal a consistent generational pattern across BNPL use, repayment difficulty, and financial literacy. Drawing on the Consumer Financial Protection Bureau, the Federal Reserve System, a LendingTree consumer survey, and the TIAA Institute-GFLEC Personal Finance Index, it documents that Generation Z and Millennials report substantially higher BNPL late-payment rates than Generation X (45 percent and 44 percent, respectively, versus 20 percent), while Generation Z separately scores lowest among generations measured on financial literacy. The two patterns only partly line up: Millennials' literacy score sits close to Generation X's even though their late-payment rate sits close to Generation Z's, so the paper treats usage, repayment, and literacy as a joint description rather than a single consistent trend. These statistics come from different samples, years, and instruments not designed to be combined, and the paper is explicit that it observes a cross-generational, cross-sectional association, not an individual-level or causal relationship. It discusses the resulting identification challenges and closes by identifying three areas (credit-bureau reporting, point-of-sale disclosure, and high school instruction on installment credit) that the pattern motivates for further policy evaluation, without claiming the data establish which intervention would work.
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This paper presents an empirical analysis of a rules-based multifactor equity portfolio and examines how its performance varies across market volatility regimes. The portfolio is constructed from a fixed universe of 50 large-cap U.S. equities and employs a cross-sectional ranking methodology based on valuation, profitability, growth, and risk signals, with momentum used only as a secondary tie-breaker. The strategy is rebalanced monthly and is evaluated relative to the SPDR S&P 500 ETF (SPY) using both gross and net-of-transaction-cost returns. Market volatility regimes are identified using the CBOE Volatility Index (VIX). Low- and high-volatility regimes are defined as the bottom and top terciles of a rolling three-month (63 trading-day) average of daily VIX levels. Portfolio performance is summarized using annualized return, annualized volatility, Sharpe ratio, and information ratio versus SPY. Results indicate pronounced regime dependence: risk-adjusted and benchmark-relative performance is strongest in low-volatility regimes, while elevated-volatility regimes exhibit substantially higher return variability and weaker benchmark-relative outcomes. This analysis is intended as an empirical research note and a methodological prototype rather than a production trading framework.
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This paper will examine the franchise economics of the Indian Premier League (IPL), as well as several other emerging franchise leagues based in the U.S. to determine if implementing a hybrid framework can help scale the Major League Cricket (MLC) in the U.S. Through analyzing existing research papers, statistics, and first-hand interviews with professional players, this paper will investigate significant differences between the two cricket leagues, roadblocks to cricket's success in America, and how to overcome them to expand the game's exposure. Player interviews, existing research papers and statistics analysis outlined repeatable tactics such as revenue sharing, media rights, star-player signings and grassroots and community developments. Despite notable setbacks, such as the lack of support for the league from the nation's governing body of cricket, limited existing exposure for the sport, and slow entry into a saturated market, cricket certainly has the potential for growth in America through the MLC and by combining what made leagues like the IPL work with researching how niche sports leagues began to break into the American market.
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05 Oct 2026
Multiple firms spanning expertise from material science and computational chemistry to pharmaceuticals have started investing into Artificial Intelligence (AI) and Large Language Models (LLMs) to streamline and expedite their Research and Discovery (R&D) process; however, the effects of this newfound integration of AI into R&D remain unclear as it is yet to be understood whether it is changing how the process is conducted versus simply accelerating the process. This study utilizes qualitative data collected from seven semi-structured interviews with interviewees with expertise spanning from corporate research and startups to innovation economics in order to analyze the central question: does the integration of AI in R&D deepen research in already explored areas by accelerating it or does it widen the directions of research and introduce novel directions? Across all interviews, the most common limitation noted in AI's integration in R&D was human validation. Regarding the central question, the results skewed towards the deepening trend. Many interviewees mentioned that the current usage of AI causes its integration to only accelerate and improve the discovery within research directions already explored; however, interviewees explained that this was a trend based on the methodology of R&D used by most firms rather than a technological barrier of the tool itself. These results suggest that firms and policymakers should thoroughly evaluate the productivity increase of AI rather than its potential and focus on solving the several bottlenecks still not tackled by AI: validation, human judgement, and physical experimentation.
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04 Oct 2026
Venture capital has become a central engine of American innovation, yet its distribution is geographically concentrated, with the vast majority of funding flowing to a handful of metropolitan hubs: Boston, New York, and the Bay Area. This paper examines how limited access to venture capital shapes a long-term economic trajectory of rural communities, arguing that the funding gap is not incidental but the product of a self-reinforcing feedback loop rooted in elite university networks, established industry clusters, and investor preference for geographic proximity. Drawing on federal and academic research on community development infrastructure capital, small business investment company (SBIC) programs, and regional economic data, this paper traces how the absence of institutional financial infrastructure in rural areas discourages entrepreneurship, accelerates "brain drain" among young talent, and locks local economies into a narrow set of low-growth industries such as agriculture and retail. This analysis also considers how the rise of AI-focused startups, which tend to generate fewer local jobs and traditional small businesses, may be widening the gap between urban and rural economic outcomes. In contrast, evidence from Community Development Venture Capital (CDVC) and SBIC-funded ventures suggests that even modest capital investment in rural areas can generate substantial employment growth and diversify local economies. Based on these findings, this paper recommends a full strategy for rural communities: cultivating community-based funding mechanisms such as crowdfunding, building durable local infrastructure including chambers of commerce, and expanding access to business lending to support entrepreneurship independent of traditional VC hubs.
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This study investigates how Haimen's land-based pension policy influences rural elderly financial behaviors and how demographic factors moderate their marginal propensity to consume (MPC). Utilizing a mixed-methods exploratory design with a sample of thirty-six participants, the research evaluates consumption responses, demographic heterogeneity, and savings motivations. The findings reveal a mean MPC of 34.7%, indicating a statistically significant positive consumption response that supports a partial Permanent Income Hypothesis (PIH) framework. Qualitative data show that spending is prioritized around a basic-needs-first hierarchy, primarily targeting food, medicine, and routine utilities, alongside modest quality-of-life enhancements. Ordinary least squares regression analysis demonstrates that the pension-to-prior-income ratio and number of dependents act as significant positive moderators of MPC, whereas age exhibits a significant negative relationship driven by habit persistence. Despite the permanent income annuity, precautionary saving remains dominant, with approximately 65% of pension income retained due to persistent structural healthcare risks and incomplete institutional safety nets. Ultimately, the study concludes that land financialization successfully relaxes daily liquidity constraints and reduces subsistence-level anxiety, but remains insufficient to replace private self-insurance in environments marked by persistent institutional healthcare gaps.
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02 Oct 2026
This paper examines how installment price framing affects consumer financial decisions. It asks whether consumers perceive a product as more affordable when sold in installments rather than as a lump sum. This paper does not collect primary data. It synthesizes published research and public data. We coded all 10 empirical evidence records for product, sample, frame, outcome, effect direction, and disclosure. The hypothesis (H1) is that presenting a purchase as smaller payments increases perceived affordability and purchase likelihood compared to presenting it as one full cost. Six of the 10 records support H1, one indirectly supports it, two produce mixed evidence, and one focuses only on disclosure. Four of the five Buy Now Pay Later (BNPL) records support H1, but one produces mixed results. Recent BNPL research has also revealed an increase in order sizes and that financial constraints are not as important. Disclosure results are less predictable. While clear information can increase understanding, it does not always lead to behavior change. The findings indicate that digital checkout design is not neutral. Small-payment framing can increase purchasing, while consumers may downplay the overall financial commitment. The findings support displaying the total price, payment terms, fees, and risk of late payment next to the installment payment. Consumers need to consider the total cost before buying and decide whether it is affordable.
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Bitcoin and U.S. equities are forecast in largely separate literatures, which leaves open whether the two markets respond to different forces or whether researchers simply study them differently. This paper imposes one framework on both: four feature blocks (Price/Technical, Sentiment/News, Activity/On-chain-or-Volume, Cross-market) evaluated across six classifier families under a walk-forward design, giving 114 model-block-asset comparisons for Bitcoin (4,346 daily returns) and for Tesla and the S&P 500 (2,338 common-calendar observations each). No comparison exceeds its majority-class base rate by a statistically significant margin after Benjamini-Hochberg correction, so daily directional predictability is not detectable for either asset class. The fitted models nevertheless draw on different information. SHAP attribution assigns 60.3% of Bitcoin's fitted signal to activity and on-chain features and 5.6% to sentiment, against price and technical dominance for Tesla (55.0%) and the S&P 500 (53.5%). Bitcoin's largest single feature is a trading-volume-based illiquidity proxy rather than a blockchain-native metric, so the model's predictions rely on trading activity broadly rather than narrowly on on-chain metrics, and the shares are inconsistent with the claim that cryptoasset prices are driven mainly by hype; because SHAP describes a fitted model rather than the price process, this is evidence about predictive reliance, not causation. A HAC-robust, false-discovery-rate-corrected Granger battery finds that Bitcoin and Tesla returns predict next-day changes in the Crypto Fear & Greed Index (F = 157.72 and F = 15.34, p < 0.001) with no reverse effect. Since volatility and momentum carry half the weight of that index, the Bitcoin result is partly mechanical; the Tesla result cannot be, and is therefore the informative one, although a Granger test establishes predictive precedence rather than the underlying mechanism. GARCH(1,1) estimates place Bitcoin's volatility persistence (α+β = 1.000) and tail thickness (Student-t ν = 3.20) above both equities.