The experiment was designed to probe one of the most persistent ambiguities in behavioral finance: do emotions influence financial performance directly, or do they operate through the behavioral mechanisms that produce performance? Decades of research have established that affective states shape judgment under uncertainty, altering attention, confidence, risk perception, and action tendencies. Prospect theory demonstrated that decision-making under risk systematically departs from rational choice assumptions, while the affect heuristic literature suggests that people often rely on immediate positive or negative feelings when evaluating risky options. This implies that emotional tone may shape investment behavior even before any trade is made. But whether these pre-trading emotional states ultimately determine returns, or simply organize how investors expose themselves to the market, has remained unclear.

To disentangle these possibilities, the researchers placed participants in a four-hour stock market simulation using the ABC Bourse platform. Each participant received virtual capital of EUR 100,000 and could trade only CAC 40 equities under realistic conditions, including transaction costs of 0.2 percent. Short selling was not allowed, but participants faced no constraints on the number of trades or position sizes. They were free to trade actively, occasionally, or remain largely in cash. The simulation incorporated an academic incentive scheme based on final portfolio value, with the best-performing participant receiving two bonus points and all others receiving at least half a point. Rankings and performance feedback were available throughout the session, introducing a tournament-like competitive element. During the four hours, the CAC 40 index rose by approximately 0.30 percent, from 8,061.40 to 8,085.76 points, meaning the market environment was broadly favorable but relatively flat.

Before the simulation began, participants completed questionnaires measuring their emotional predispositions on five-point Likert scales. The researchers constructed six composite indices: positive and negative pre-trading emotional tone measured through PANAS items, positive and negative simulation-specific emotional expectations tied to the upcoming trading task, and two broader global composites aggregating the corresponding positive and negative dimensions. All six indices displayed strong internal consistency, with Cronbach’s alpha values ranging from .851 to .933. Importantly, the positive and negative poles were only weakly correlated with each other, suggesting that participants could simultaneously report high levels of one and low levels of the other, or various combinations in between. This justified retaining both poles as distinct dimensions rather than treating them as statistical mirror images.

The empirical strategy proceeded in two stages. First, the researchers examined direct associations between each of the six emotional predisposition indices and portfolio return, calculated as the percentage change between final and initial portfolio value. Second, they conducted exploratory mediation-style analyses testing whether each index was statistically connected to return through four behavioral indicators: total number of transactions, average transaction volume, portfolio standard deviation, and average cash holdings relative to initial capital. These models retained the formal structure of mediation analysis but were interpreted as exploratory rather than causal, because the behavioral indicators resulted from participants’ own trading decisions rather than random assignment. Bootstrap confidence intervals based on 5,000 resamples were used to assess estimated indirect paths.

The results were unambiguous on the performance side. None of the six pre-trading emotional predisposition indices was significantly associated with portfolio return, and no significant estimated indirect path emerged through any of the four behavioral indicators. The strongest bivariate correlation between an emotional index and return was weak and non-significant. This finding persisted even after the researchers applied a Benjamini-Hochberg false discovery rate correction across the 30 primary associations and after re-estimating models with participant-level controls including gender, age, prior stock-market experience, and the five OCEAN personality traits. The absence of significant return associations should be interpreted cautiously, however, given that the simulation lasted only four hours and portfolio returns were highly compressed around zero, with a mean return of -0.001 and a standard deviation of only 0.003.

By contrast, the emotional indices were consistently and significantly associated with trading-style indicators. Positive simulation-specific emotional expectations were significantly associated with larger average transaction volume, greater portfolio standard deviation, and lower average cash holdings, fully supporting the hypothesis that positive predispositions orient participants toward a more exposed trading style. Negative pre-trading emotional tone was significantly associated with smaller average trades, lower portfolio variability, and higher cash retention, fully supporting the corresponding defensive hypothesis. The global negative emotional predisposition composite showed the same pattern, correlating at -.236 with average transaction volume, -.263 with portfolio standard deviation, and .328 with average cash holdings. These associations point toward two contrasting configurations of market engagement: positive predispositions seem to organize a more engaged posture characterized by stronger capital commitment and greater willingness to accept variability, while negative predispositions organize a more defensive posture marked by smaller positions and a stronger tendency to preserve cash.

One behavioral indicator did carry a directional expectation regarding performance, and it was confirmed. The number of transactions was significantly and negatively associated with portfolio return, making trading intensity the most stable performance-related result in the study. This finding aligns with prior research showing that greater trading activity is not associated with better outcomes among individual investors, as more active investors tend to underperform. However, none of the six emotional predisposition indices significantly predicted the number of transactions, meaning that emotions did not explain why some participants traded more frequently than others. The behavioral indicators most closely associated with pre-trading emotional predispositions, such as position size and cash retention, did not significantly predict return, which helps explain why the mediation-style analyses did not reveal significant indirect paths.

From a psychological perspective, the researchers interpret this distinction in terms of action readiness. Positive pre-trading emotional climates appear to be associated with engagement and exploratory, approach-oriented behavior, consistent with the broaden-and-build theory of positive emotions. Negative pre-trading emotional climates appear to be associated with caution and defensive orientations, consistent with appraisal theories showing that different negative emotions produce distinct behavioral consequences. This does not imply that positive emotions necessarily improve decision quality, nor that negative emotions necessarily reduce it. It suggests that these states orient investors toward different ways of confronting uncertainty. Because the participants were novice investors with limited prior market experience, they were less likely to rely on established trading routines and more likely to express their psychological orientation directly through their behavior, making pre-trading emotional predispositions visible in trading style rather than neutralized by habit.

The study’s conclusions are bounded by several limitations. The simulation environment simplified some features of real financial markets, the participants were undergraduate students rather than professional traders, and the four-hour horizon may be too short for broad emotional predispositions to produce stable performance effects. The study was also powered to detect moderate associations but may have been insufficient to detect small ones, particularly in the exploratory mediation-style analyses. The academic bonus points and live rankings may have amplified competitive behavior and social comparison. Nevertheless, the central contribution stands: pre-trading emotional predispositions are more closely associated with how novice investors engage with the market than with the returns they eventually achieve. The findings suggest that greater attention should be paid to how emotions organize the style, intensity, and risk profile of trading behavior, rather than focusing only on whether they improve or reduce performance. In the early formation of investor psychology, it seems, feelings write the posture before they write the profit.

Subject of Research: Trading style rather than return: the role of pre-trading emotional predispositions in a stock market simulation

Article Title: Trading style rather than return: the role of pre-trading emotional predispositions in a stock market simulation

Article References: Finet, A., Kristoforidis, K., & Laznicka, J. (2026). Trading style rather than return: the role of pre-trading emotional predispositions in a stock market simulation. Frontiers in Behavioral Economics, 5, Article 1836906. https://doi.org/10.3389/frbhe.2026.1836906

Image Credits: AI Generated

DOI: 10.3389/frbhe.2026.1836906

Keywords: Trading, style, rather, than, return, role, pre-trading, emotional, predispositions, stock, market, simulation

The research team, led by Ramalingareddy Vijaya Kumari and Sreenivas Akula at Professor Jayashankar Telangana State Agricultural University in Hyderabad, combined decades of official agricultural statistics with sophisticated econometric tools normally reserved for financial markets. They applied compound annual growth rates, decomposition analysis, and instability indices to measure how area, output, and yield changed over time. Then they turned to wholesale price data from January 2020 to December 2025, sourced from Agmarknet and the Department of Consumer Affairs, to probe the invisible architecture of India’s sesame trade using unit root tests, Johansen co-integration, Granger causality, and Vector Error Correction Models.

The headline result is striking: India’s sesame production growth has been driven almost entirely by yield improvements rather than land expansion. Decomposition analysis showed that at the national level, the yield effect contributed 106.34 percent of production growth while the area effect was actually negative at −1.42 percent. In practical terms, farmers are coaxing more grain from less soil. Productivity rose from roughly 368 kilograms per hectare in 1980–81 to 613 kilograms per hectare in 2025–26, reflecting the adoption of improved varieties and better crop management practices. Meanwhile, the cultivated area contracted from about 2,508 thousand hectares to roughly 1,526 thousand hectares over the same span.

Not every state followed the same script. West Bengal and Madhya Pradesh recorded consistent production growth of 3.82 and 3.96 percent respectively over the full study period, with moderate instability. Telangana, by contrast, saw its area shrink at −4.07 percent annually, yet productivity surged at 5.21 percent per year, meaning technology and management largely compensated for lost acreage. Gujarat’s productivity climbed 2.43 percent despite wild swings in production, which registered an instability index of 48.51. Rajasthan, once a sesame stronghold, posted a substantial negative area effect of −58.41 percent in the overall decomposition, suggesting farmers there have shifted toward more remunerative crops like paddy, wheat, and soybean where assured procurement exists.

Price behavior across the five major markets told its own seasonal story. Using a 12-month moving average and multiplicative time-series decomposition, the researchers found that prices generally dipped during peak arrival months from March through June, when fresh harvests flooded the markets, and peaked during lean supply periods from September through December. Rajasthan showed the sharpest seasonal variation, with prices in November reaching 112.15 percent of the annual average while June bottomed out at 86.22 percent. The Cuddy–Della Valle Index revealed that instability was most pronounced during the monsoon months of June through September, with Telangana recording the highest monthly figure of 24.04 percent in July. West Bengal remained comparatively stable throughout the year, suggesting better storage behavior or more consistent market arrivals.

The real technical fireworks came from the market integration analysis. Correlation coefficients between state-level wholesale prices ranged from 0.88 to 0.95, all highly significant, indicating that sesame prices moved in near-lockstep across geographically separated markets. Even after stripping out trend and systematic effects, residual correlations stayed between 0.854 and 0.940, confirming strong short-run linkages. The Augmented Dickey–Fuller test showed all price series were non-stationary at level but became stationary after first differencing, classifying them as integrated of order one, I(1). This satisfied the prerequisite for Johansen co-integration testing, which rejected the null hypothesis of no co-integration at every level, with a trace statistic of 132.86 for the “None” hypothesis and a p-value of 0.000.

Co-integration means the markets share a stable long-run equilibrium, even if prices temporarily drift apart. The Vector Error Correction Model then quantified how quickly deviations get corrected. Madhya Pradesh showed a significant negative error-correction coefficient of −0.194, meaning roughly 19.4 percent of any disequilibrium from the previous period was eliminated each period. Telangana (0.193) and Gujarat (0.250) also displayed statistically significant adjustment responses. Rajasthan and West Bengal, however, showed no significant error-correction behavior, suggesting they respond to price signals differently or rely on other mechanisms to restore balance. Residual diagnostics confirmed the model was sound, with no autocorrelation or heteroskedasticity issues, though residuals were not perfectly normally distributed.

Granger causality tests then mapped the direction of price information flow, revealing a predominantly one-way transmission network. Madhya Pradesh, Gujarat, Rajasthan, and Telangana all significantly Granger-cause West Bengal, while the reverse relationships were insignificant. Gujarat, Rajasthan, and Telangana also influence Madhya Pradesh. Rajasthan transmits price signals to Gujarat, and Telangana influences both Gujarat and Rajasthan. In short, Rajasthan, Gujarat, and Telangana act as price leaders, while West Bengal and Madhya Pradesh behave as price followers. The impulse response function showed that a one-standard-deviation shock in Rajasthan produced a strong immediate response there, peaked in the third month, and then propagated to Madhya Pradesh, which responded positively until the fifth month before moderating. West Bengal showed only a mild, stable reaction, while Telangana fluctuated, starting with a brief negative response before a sharp positive increase.

Forecast error variance decomposition added another layer of nuance. Rajasthan’s own shocks accounted for 46.61 percent of its price variability in the first period, but this declined to 37.82 percent by the tenth period, indicating that external influences grow over time. Madhya Pradesh emerged as the dominant external contributor, rising from 24.32 percent to 28.83 percent, while Telangana’s influence climbed from zero to 13.26 percent. Gujarat’s contribution fell from 22.93 percent to 11.58 percent, and West Bengal remained the weakest influence at 8.52 percent. These results suggest that no single market dominates all aspects of price transmission; leadership differs between short-run signaling and longer-term influence, which is useful intelligence for procurement planning and market monitoring.

On the trade front, India remains a major net exporter of sesame, but the picture is shifting. Exports peaked at 389.15 thousand tonnes in 2011 before declining to 218.97 thousand tonnes in 2023. Imports, negligible until 2012, surged thereafter, reaching 149.10 thousand tonnes in 2020 and 124.98 thousand tonnes in 2023. This dual pattern of significant exports and rising imports reflects growing domestic demand, periodic supply shortages, and intensifying global competition rather than a simple decline in India’s export role. World sesame production in 2024–25 is estimated at 6.68 million tonnes, down 7.7 percent year-on-year, and India ranks first in production with 8.66 lakh tonnes, ahead of Myanmar, Sudan, Nigeria, and Pakistan.

The study’s authors argue that sustainable growth of the sesame sector requires a strategic focus on climate-resilient cultivars suited to diverse agro-ecological conditions, stronger post-harvest infrastructure including storage and processing, and wider adoption of digital marketing platforms like e-NAM to improve transparency and price discovery. Given rising consumer interest in cold-pressed sesame oil, driven by perceived health benefits and the presence of natural antioxidants such as sesamin and sesamol, value-chain strengthening and export promotion could unlock significant economic potential. Encouraging sesame cultivation in rainfed and resource-constrained regions through appropriate policy support may foster crop diversification, improve farm profitability, and contribute to sustainable agricultural development across India.

Subject of Research: Production dynamics, trade behavior and spatial market integration of sesamum in India

Article Title: Production dynamics, trade behavior and spatial market integration of sesamum in India

Article References: Vijaya Kumari, R., Akula, S., Gundu, R., & Panasa, V. (2026). Production dynamics, trade behavior and spatial market integration of sesamum in India. Frontiers in Sustainable Food Systems, 10, Article 1938242. https://doi.org/10.3389/fsufs.2026.1938242

Image Credits: AI Generated

DOI: 10.3389/fsufs.2026.1938242

Keywords: Production, dynamics, trade, behavior, spatial, market, integration, sesamum, India, scientific research