A growing body of work explores how Large Language Models (LLMs) can be embedded in trading systems as agents that perceive market information, retrieve context, reason about decisions, emit tradable actions, and adapt under market feedback. This paper reframes LLM-based trading agents as expert-system decision pipelines and presents an audit-oriented evidence map of 77 included studies in a protocol-coded snapshot screened through 2026-03-09. A primary empirical subset (n=19) satisfies the minimum boundary of Action Output plus Closed-Loop Evaluation; the remaining 58 included studies are retained as background and design context. The central empirical finding is protocol incomparability: within the primary subset, only 2/19 studies report extractable time-consistent split protocols, 1/19 reports an explicit transaction-cost model, 1/19 documents universe or survivorship handling, 11/19 report execution timing or semantics, 15/19 are coded as R0, and no study reaches R3 reproducibility. We therefore use Architecture-Capability-Adaptation as a working analytical lens rather than a validated taxonomy, and we foreground the evidence ledger, reproducibility audit, and reporting checklist as the main contributions. The resulting survey shows that architectural experimentation is expanding rapidly, while comparable evaluation protocols, execution semantics, and reproducible artifacts remain the field's immediate bottlenecks.

We document international evidence on household affordability of deep decarbonization by applying a simple formula for an affordability index to publicly available market data. We conclude that economy-wide deep decarbonization policies are affordable for average households of the International Energy Agency's member regions and therefore politically feasible for these regions.

We use Stanford University's two lists of top 2% researchers (T2R) based on these researchers' career-long and single recent year impacts to find that (a) a top 100 university's numbers of T2R move the university's global rankings published by five ranking agencies; and (b) the elasticity between a university's ranking and its number of T2R is approximately -1.0 and statistically equal across the five agencies. This means that a 1% increase in a university's T2R count leads to an estimated 1% improvement in its ranking position.

We study the firm dynamics associated with mergers and acquisitions (M&A) and their implications at the micro and macro levels. Our paper presents three main findings: (i) mergers generate a more fat-tailed firm-size distribution, thereby amplifying granular fluctuations and increasing aggregate volatility; (ii) the impact of mergers depends on strategic market power and endogenous markups; and (iii) under endogenous markups, we provide a novel characterization of the firm size-volatility relationship in which volatility declines disproportionately with size. We build a quantitative model of domestic horizontal mergers and find a sizeable impact of mergers on aggregate volatility using counterfactual analysis.

Shenzhen’s residential inclining tier rates are designed to encourage electricity conservation. However, the rates for customers with relatively low consumption fall below Shenzhen’s estimated marginal generation cost, leading to economically inefficient overconsumption. Therefore, we propose an incentive scheme with voluntary participation to promote residential electricity conservation. We further demonstrate that this scheme is Pareto superior, as its implementation can benefit participating customers without adversely affecting the electric utility or non-participating customers.

Texas is the largest electricity-consuming state in the United States and leads thenation in variable renewable energy (VRE) development. It projects a huge increasein solar plant construction, despite VRE development's "cannibalization effect" onthe investment incentive for solar generation and the rising popularity of short-term VRE power purchase agreements in the United States. Our empirical investigationof short-term spot and forward solar energy sales first uses Texas's monthly whole-sale electricity market data for February 2016 to December 2021 to forecast theaverage daytime (07:00-19:00) spot energy prices and their standard deviations forforward-looking periods of one year, three years, five years and ten years. It thenapplies the price forecast results to analyze the revenue forecasts for a solar gener-ation developer's spot and forward energy sales, revealing that a new solar plant'srevenue forecast level (respectively, volatility) increases (respectively, decreases)with a short-term solar power purchase agreement's forward energy price. When theforward energy price is below (respectively, above) the spot energy price forecast,the developer's short-term power purchase agreement offer in response to a load-serving entity's VRE procurement auction announcement is for a megawatt-fraction(respectively, 100%) of the plant's energy output.

We use a profit-maximizing model of forward pricing, which revealed that an electricity retailer’s fixed price offer (FPO) contains a large forward premium that encourages vertical integration of a generation company and a retailer. However, vertical integration does not always reduce FPOs, particularly when residential customers are segmented by consumption size and price sensitivity. Hence, a proposed merger of a big generation company and a big retailer requires regulatory scrutiny.

Accurate estimates of the price responsiveness of residential, commercial, and industrial electricity demands are essential for energy policy modelling, integrated resource planning, and determining a competitive wholesale electricity market’s generation levels, prices, and capacity investments. Hence, we estimate the own-price elasticities of solar and wind capacity demands of a load serving entity (LSE) that provides retail electricity service, thereby answering two interrelated research questions: (1) does solar capacity demand far exceed wind capacity demand? and (2) are solar and wind capacity demands price-elastic? Inspired by the theory of input demand under input price uncertainty, our innovative methodology integrates (a) wholesale spot energy price forecasts by time of day; (b) pseudo data found by minimizing a LSE’s annual risk-adjusted budget for procuring solar and wind capacities; and (c) econometric analysis of (b) to estimate the extent of substitutability between solar and wind capacities and the own-price elasticities of solar and wind capacity demands. Using Texas as an illustrative example, we find that when solar and wind power purchase agreements have similar energy prices, solar capacity demand is approximately four times wind capacity demand. Further, the own-price elasticity estimates are -5.34 for solar capacity demand and -5.65 for wind capacity demand. As a result, solar and wind capacity demands tend to substantially grow (shrink) in response to declining (rising) solar and wind energy prices. This lends support to proposals to raise solar and wind energy prices for mitigating the adverse effects of large-scale variable renewable energy development on an electric grid’s efficient operation and system reliability. However, adopting such proposals also slows the grid’s pace of decarbonization, thus underscoring the policy and regulatory challenges in the quest for a clean and sustainable electricity future. Hence, our policy recommendation of price managing solar and wind capacity demands is a topic of policy debate that deserves the attention of an electric grid’s stakeholders.

Electricity outage cost ( EOC ) estimates ($ per kWh unserved) are essential input data for optimal reliability planning and efficient pricing of electricity services. Based on the 2019-2020 market data published by two US government agencies for the lower 48 states, this paper’s EOC estimates by census region and year are median values of $1.39 to $2.93 per kWh unserved, well below the estimate of $9 per kWh unserved adopted by Texas for optimal reliability planning. The policy implications of adopting our lower EOC estimate are (a) a reduction in an electric grid’s optimal planning reserve to improve the grid’s cost efficiency; and (b) a decline in the grid’s marginal cost-based retail price to encourage welfare-enhancing end-use consumption.

Texas's windfarm construction continues unabated, despite wind energy's cannibalization effect on wind generation's investment incentive and the rising popularity of short-term wind power purchase agreements (PPAs). Using ERCOT's monthly data for Jan-2011 to Dec-2021, we develop spot energy price forecasts by time of day (TOD) and their standard deviations to derive the efficient frontiers (EFs) for spot and forward energy sales of a risk-averse windfarm developer under a short-term wind PPA of not more than ten years. These EFs reveal a windfarm's operating revenue forecast tends to increase with the PPA's forward energy prices. Further, the windfarm's revenue risk and forecast move in tandem, akin to the risk-return relationship rooted in Markowitz's portfolio theory. Hence, the developer tends to sell one hundred percent (<100%) of the windfarm's energy output at forward energy prices that are above (below) spot energy price forecasts by TOD for recovering wind generation's levelized cost of energy.

In this paper we project how much incremental wind energy development may occur without causing inadequate investment incentives (also known as missing money) for wind generation and natural-gas-fired generation in the day-ahead market and real-time market of the Midcontinent Independent System Operator (MISO) in the United States. Using a large sample of hourly data for the 82-month period of January 1, 2014 to October 31, 2020, we document that the day-ahead market's hourly investment incentives move with the day-ahead forecast of daily natural gas prices;MISO's day-ahead hourly requirements of ancillary services; MISO's zonal day-ahead hourly schedules of nuclear generation, wind generation and must-run generation; and MISO's zonal day-ahead forecasts of hourly loads. Findings based on the real-time market's hourly data tell a similar story. Further, the negative effect of incremental wind energy development on investment incentives over the forward looking period of 2023-42 is offset by the positive effect of a rising natural gas price, nuclear plant retirement, declining must-run generation and growing demand. In the extreme case of nuclear plant retirement and zero must-run generation, incremental wind energy development of up to around 441% of the existing level of wind generation may occur as a market-based outcome without missing money in MISO's day-ahead market.

This paper uses an experiment to identify what modeling decisions significantly affect estimates of own-price elasticity for non-residential (commercial and industrial) electricity demand in the United States (U.S.). Based on 174,240 panel data model runs involving 10,944 monthly state-level observations from the Energy Information Administration for 2001–2019, these decisions are parametric specification, estimation method, and treatment of cross-section dependence. As most of the many generated elasticity estimates are between 0.0 and −0.2, price-induced conservation is likely modest, thus justifying continued policy support for energy efficiency standards and demand-side management in the U.S. path to deep decarbonization.

We modify the Eaton and Kortum (2002) model to a multi-sector general equilibrium Ricardian model with fragmentation and input-output linkages. After calibrating the model, we carry out counterfactual exercises to quantify the impacts of the changes in the exogenous variables on gains from trade (GFT) of countries, and carry out variance decomposition to quantify the contribution of each factor. Then, we carry out some policy counterfactuals. The results are: 1. Global changes in trade costs and technology stocks explain about 97% of the variance of the changes in GFT of nations. 2. The estimated change in GFT from a model that takes into account fragmentation is on average 53% higher than that estimated by a model that does not. 3. The average elasticity of GFT w.r.t. domestic changes in technology stocks (trade costs) in intermediate goods is three (two) times that of domestic changes in final goods technology (trade costs). 4. International spillovers contributed to 65% of the changes in domestic GFT of countries. 5. Export-biased technological changes accounted for 48% of the increases in domestic GFT of nations. 6. Heat maps showing "friends" and "enemies" in international trade indicates that China has most friends. It accounts for 31% of total international spillovers from all countries.

We estimate the impact of introducing retail competition on retail electricity prices paid by residential consumers in Texas's two largest cities, Dallas and Houston. Using the synthetic control method to obtain the counterfactual prices, we find that retail competition raised average prices by $0.0112/kWh ($11.2/MWh) in the transition period from 2001 to 2006 and by $0.0134/kWh ($13.4/MWh) during the period of unfettered competition from 2007 to 2020. However, when the US wholesale natural gas prices are relatively low, actual retail electricity prices in areas opened to retail competition are close to the counterfactual prices that would have prevailed had retail competition not been introduced, as measured by the counterfactual prices estimated using the synthetic control method.

Effective regulatory policies, ample renewable energy potential, and a favorable business climate have led to a boom in wind farms and solar energy projects in Texas. Using a large sample of 15-min data for the six-year period of 2016–2021, we analyze per MWh revenues from solar generation and wind generation in the renewable regions of the Electric Reliability Council of Texas (ERCOT). We find that average regional revenues from variable renewable energy (VRE) for the daytime hours of 07:00–19:00 exceed the average levelized price of recently signed solar and wind power purchase agreements (PPAs), although average regional revenues for wind generation for the nighttime hours of 19:00–07:00 were lower than recent PPA prices. Moreover, VRE's 15-min regional revenues move with ERCOT's cap on the real-time market's energy price offers, regulatory price adder, daily wholesale natural gas price, 15-min nuclear energy generation, 15-min system load, 15-min regional solar energy and 15-min regional wind energy. While continued VRE development may suppress wholesale market prices, continued growth in electricity demand and increasing natural gas prices provide offsetting effects. Nevertheless, new policies are under development to mitigate the adverse effects of further VRE development on grid reliability and generation investment incentive.

Using a large sample of 15-minute data for the 6-year period of 2016-2021, we analyse solar generation’s (SG’s) and wind generation’s (WG’s) per MWh revenues (“revenues” for short hereafter) in the renewable regions of the Electric Reliability Council of Texas (ERCOT). We find that SG’s and WG’s regional average revenues for the day-time hours of 07:00-19:00 exceed the average levelized price of recently signed solar and wind power purchase agreements. However, the same cannot be said about WG’s regional average revenues for the night-time hours of 19:00-07:00. Further, SG’s and WG’s 15-minute regional revenues by time of day move with ERCOT’s cap on the real-time market’s energy price offers, Texas’s regulatory price adder, the US daily wholesale natural gas price, Texas’s 15-minute nuclear generation output, ERCOT’s 15-minute system load, ERCOT’s 15-minute output of regional SG, and ERCOT’s 15-minute output of regional WG. Importantly, SG’s and WG’s revenue reduction ( aka cannibalization) effects in the 2023-2042 period are projected to be offset by revenue increases caused by rising natural gas price, growing demand, and nuclear plant retirement. Hence, market forces may suffice to sustain SG’s and WG’s large-scale development for decarbonizing Texas’s electricity future.

The efficient market hypothesis (EMH) is a fundamental tenet of active and unfettered market trading. The EMH for intertemporal trading of a commodity such as natural gas implies that today’s futures price is an unbiased predictor of a future delivery period’s spot price and that the difference between today’s futures and spot prices reflects the commodity’s cost of carrying. The EMH for inter-regional trading requires the commodity’s regional price difference to equal the inter-regional transportation cost. Using regional hourly data for January 1, 2011 to December 31, 2020 from the Electric Reliability Council of Texas (ERCOT), we investigate whether the EMH is empirically valid in all ERCOT’s wholesale electricity markets and, if not, the extent of ERCOT’s energy trading inefficiency and what can be done to reduce it. By estimating a parsimonious system of eight price-level regressions and four price-difference regressions, we reject the interday EMH for all regions and the inter-regional EMH for all regional market pairs. However, ERCOT’s extent of trading inefficiency is mild when compared to the wholesale energy prices. Our empirical examples have two policy implications: enhancing ERCOT’s interday trading efficiency entails improving the day-ahead forecasts for solar and wind generation and refining ERCOT’s indicative real-time market prices, while enhancing ERCOT’s inter-regional trading efficiency requires transmission capacity expansion to reduce transmission congestion and line losses.

Efficient trading under wholesale market competition reduces an electric grid's energy costs for meeting timeand location-dependent demands. We analyse energy trading efficiency by estimating three newly developed sets of energy price difference regressions interconnected by their common root of energy price level regressions. Using a sample of similar to 0.6 million hourly observations for 01/01/2014 to 10/31/2020, our estimation documents energy trading efficiency of the 10 local resource zones across 15 American states administered by the Mid-continent Independent System Operator (MISO). We find MISO's zonal energy markets integrated across space (zone j vs. zone k for j (sic) k) and time (day-ahead market vs. real-time market). However, these markets exhibit inter-day energy prices differences that move with the fundamental drivers (e.g., day-ahead forecasts for natural gas price and zonal wind generation) of day-ahead energy prices and their forecast errors. Inter-zonal day-ahead energy price differences move with the fundamental drivers and their zonal variations. Inter-zonal real-time energy price differences increase with inter-zonal day-ahead energy price differences and depend on zonal variations of the fundamental drivers' forecast errors. Our empirics yield two important policy implications. First, enhancing inter-day trading efficiency requires accuracy improvements in (a) day-ahead forecasts for natural gas price, zonal load levels, and zonal wind generation, and (b) day-ahead scheduling of zonal nuclear and must-run generation. Second, improving inter-zonal trading efficiency requires mitigating inter-zonal transmission congestion via transmission capacity expansion, generation investment and demand reduction in a load pocket, and virtual bidding for inter-zonal energy price differences.

We develop a simple formula for computing the global welfare effect of reduction in bilateral trade costs, such as shipping costs or the costs of administrative barriers to trade. The formula is applicable to a broad class of perfect competition and monopolistic competition models and settings, including perfect competition with multi-stage production and Melitz’s (Econometrica 71(6):1695–1725, 2003) model with general firm productivity distribution. We prove that the underlying mechanism is the envelope theorem. We then extend our analysis to models with non-constant markups. Finally, we carry out some empirical applications to show the user-friendliness of the formula.