++

prepare_prompt/alpha_prompt.txt

@@ -1,14 +1,17 @@
 任务指令
-Residual Value Fluctuation Arbitrage Factor
+Receivables Quality Premium Factor
 
 Hypothesis
-In capital-intensive industries utilizing operating leases (e.g., airlines, retail, industrial equipment), fluctuations in the estimated residual value of leased assets reflect market divergence regarding the assets' long-term worth. An upward, out-of-expectations revision in the residual value estimate disclosed in financial reports signals increased optimism from management or appraisers concerning the asset's economic life, technological obsolescence risk, or secondary market liquidity. This optimism may precede an actual improvement in the asset's realizable value or a reduction in discounting costs, serving as a potential positive signal. Conversely, a downward, unexpected revision may foreshadow potential asset impairment and future cash flow erosion.
 
-Implementation
-Construct a "Residual Value Revision Intensity" metric using data from financial statement notes related to "Lease Liabilities," specifically the present value of future minimum lease payments and the estimated residual value of leased assets. The calculation is: (Current Period Disclosed Residual Value Estimate - Prior Period Estimate) / Total Carrying Value of Leased Assets. Employ a time-series breakout detection operator (e.g., ts_breakout or residual analysis) to identify anomalous adjustment points where the estimate deviates from its historical trend. For companies identified with a positive anomalous adjustment, gradually establish a long position after a defined lag period (e.g., 1-3 months) following the disclosure. For those with a negative anomalous adjustment, establish a short position. Use a time-series backfill operator to ensure data continuity.
+Receivables quality (rather than mere scale) is a core reflection of an enterprise's operational resilience and bargaining power. High-quality receivables—characterized by short collection cycles, low bad debt rates, and a reasonable proportion of receivables from major customers—indicate strong certainty in cash flow collection, low operational risks, and significant bargaining power over downstream customers. Such companies are more likely to achieve stable profit growth and obtain long-term valuation premiums, warranting long positions. Conversely, companies with poor receivables quality face potential risks of cash flow pressure and increased impairment losses, resulting in weak performance certainty, and short positions should be established.
+
+Implementation Plan
+
+Construct a "composite receivables quality indicator" integrating three core dimensions: receivables turnover days, allowance for bad debts ratio, and the proportion of receivables from the top five customers. Standardize data of each dimension through time-series standardization processing, and generate a comprehensive quality score using the weighted scoring method (weights are set based on the sensitivity of each dimension to cash flow). Utilize time-series trend operators to analyze the variation trajectory of quality scores over the past four quarters, assign positive weights to companies with scores consistently at a high level and steadily improving, and negative weights to those with scores persistently low or rapidly declining. Meanwhile, eliminate the interference of industry scale differences on the indicator through standardization processing.
 
 Alpha Factor Optimization Suggestions
-The efficacy of this factor is likely highly dependent on asset characteristics. It is recommended to introduce asset-segmentation grouping: for instance, group leased assets by type (aircraft, vessels, retail storefronts, IT equipment) and compute the cross-sectional percentile of the Residual Value Revision Intensity within each group. Additionally, overlay industry capacity utilization or secondary asset transaction price momentum as confirming signals. Enhance the factor weight when a residual value upward revision coincides with improving activity in the corresponding asset's secondary market. Conversely, attenuate the signal when these metrics diverge.
+
+Introduce customer credit rating data as a moderating variable; appropriately increase the weight of receivables quality scores for companies whose downstream customers are mostly high-credit entities to enhance factor effectiveness. 2. Dynamically adjust the weights of each dimension according to industry characteristics (e.g., focusing on turnover days in the retail industry and bad debt rates in the manufacturing industry) to replace the unified weighting model and improve industry adaptability. 3. Incorporate cash flow verification operators; strengthen signal weights when there is positive resonance between receivables quality scores and operating cash flow net growth rate, and weaken signals when there is a divergence, so as to reduce misjudgment risks of a single indicator.
 
 *=========================================================================================*
 

prepare_prompt/keys_text.txt

@@ -1,10 +1,13 @@
-**Residual Value Fluctuation Arbitrage Factor**
+Receivables Quality Premium Factor
 
-**Hypothesis**
-In capital-intensive industries utilizing operating leases (e.g., airlines, retail, industrial equipment), fluctuations in the estimated residual value of leased assets reflect market divergence regarding the assets' long-term worth. An upward, out-of-expectations revision in the residual value estimate disclosed in financial reports signals increased optimism from management or appraisers concerning the asset's economic life, technological obsolescence risk, or secondary market liquidity. This optimism may precede an actual improvement in the asset's realizable value or a reduction in discounting costs, serving as a potential positive signal. Conversely, a downward, unexpected revision may foreshadow potential asset impairment and future cash flow erosion.
+Hypothesis
 
-**Implementation**
-Construct a "Residual Value Revision Intensity" metric using data from financial statement notes related to "Lease Liabilities," specifically the present value of future minimum lease payments and the estimated residual value of leased assets. The calculation is: (Current Period Disclosed Residual Value Estimate - Prior Period Estimate) / Total Carrying Value of Leased Assets. Employ a time-series breakout detection operator (e.g., `ts_breakout` or residual analysis) to identify anomalous adjustment points where the estimate deviates from its historical trend. For companies identified with a positive anomalous adjustment, gradually establish a long position after a defined lag period (e.g., 1-3 months) following the disclosure. For those with a negative anomalous adjustment, establish a short position. Use a time-series backfill operator to ensure data continuity.
+Receivables quality (rather than mere scale) is a core reflection of an enterprise's operational resilience and bargaining power. High-quality receivables—characterized by short collection cycles, low bad debt rates, and a reasonable proportion of receivables from major customers—indicate strong certainty in cash flow collection, low operational risks, and significant bargaining power over downstream customers. Such companies are more likely to achieve stable profit growth and obtain long-term valuation premiums, warranting long positions. Conversely, companies with poor receivables quality face potential risks of cash flow pressure and increased impairment losses, resulting in weak performance certainty, and short positions should be established.
 
-**Alpha Factor Optimization Suggestions**
-The efficacy of this factor is likely highly dependent on asset characteristics. It is recommended to introduce **asset-segmentation grouping**: for instance, group leased assets by type (aircraft, vessels, retail storefronts, IT equipment) and compute the cross-sectional percentile of the Residual Value Revision Intensity within each group. Additionally, overlay **industry capacity utilization** or **secondary asset transaction price momentum** as confirming signals. Enhance the factor weight when a residual value upward revision coincides with improving activity in the corresponding asset's secondary market. Conversely, attenuate the signal when these metrics diverge.
+Implementation Plan
+
+Construct a "composite receivables quality indicator" integrating three core dimensions: receivables turnover days, allowance for bad debts ratio, and the proportion of receivables from the top five customers. Standardize data of each dimension through time-series standardization processing, and generate a comprehensive quality score using the weighted scoring method (weights are set based on the sensitivity of each dimension to cash flow). Utilize time-series trend operators to analyze the variation trajectory of quality scores over the past four quarters, assign positive weights to companies with scores consistently at a high level and steadily improving, and negative weights to those with scores persistently low or rapidly declining. Meanwhile, eliminate the interference of industry scale differences on the indicator through standardization processing.
+
+Alpha Factor Optimization Suggestions
+
+1. Introduce customer credit rating data as a moderating variable; appropriately increase the weight of receivables quality scores for companies whose downstream customers are mostly high-credit entities to enhance factor effectiveness. 2. Dynamically adjust the weights of each dimension according to industry characteristics (e.g., focusing on turnover days in the retail industry and bad debt rates in the manufacturing industry) to replace the unified weighting model and improve industry adaptability. 3. Incorporate cash flow verification operators; strengthen signal weights when there is positive resonance between receivables quality scores and operating cash flow net growth rate, and weaken signals when there is a divergence, so as to reduce misjudgment risks of a single indicator.