Datasets:

sovai
/

factor_signals

Modalities:

Tabular

Text

Formats:

Size:

Libraries:

Dataset card Viewer Files Files and versions Community

Dataset Viewer

Auto-converted to Parquet

Full Screen Viewer

Full Screen

Split (1)

train · 8.97M rows

ticker stringlengths 1 10	date timestamp[ns]	profitability float32 1 100	value float32 1 100	solvency float32 1 100	cash_flow float32 1 100	illiquidity float32 1 100	momentum_long_term float32 1 100	momentum_medium_term float32 1 100	short_term_reversal float32 1 100	price_volatility float32 1 100	dividend_yield float32 1 100	earnings_consistency float32 1 100	small_size float32 1 100	low_growth float32 1 100	low_equity_issuance float32 1 100	bounce_dip float32 1 100	accrual_growth float32 1 100	low_depreciation_growth float32 1 100	current_liquidity float32 1 100	low_rnd float32 1 100	momentum float32 1 100	market_risk float32 1 100	business_risk float32 1 100	political_risk float32 1 100	inflation_fluctuation float32 1 100	inflation_persistence float32 1 100	returns float32 -1 1.44k ⌀
A	1999-11-26T00:00:00	57	8	18	77	82	61	57	47	29	29	64	3	50.5	1	52	54	45	97	74.5	61	13	26	50	69	37	0.020071
A	1999-12-03T00:00:00	56	8	18	77	82	61	57	16	29	29	64	3	50.5	1	52	54	45	96	74.5	61	13	26	50	69	37	0.080332
A	1999-12-10T00:00:00	56	8	21	77	82	61	57	30	28	28	64	3	50.5	1	52	54	45	96	74.5	60	13	26	50	69	37	0.005629
A	1999-12-17T00:00:00	56	7	24	77	82	61	57	20	11	11	64	3	50.5	1	52	54	45	96	74.5	60	13	26	50	69	37	0.026599
A	1999-12-24T00:00:00	55	7	14	77	82	61	57	12	13	13	64	3	50.5	1	52	54	45	96	74.5	61	13	26	50	69	37	0.082932
A	1999-12-31T00:00:00	53	4	10	77	82	61	57	1	97	97	64	2	50.5	1	52	54	45	95	74.5	61	13	26	50	69	37	0.553972
A	2000-01-07T00:00:00	54	5	24	77	82	61	57	34	97	97	64	2	50.5	1	52	54	45	96	74.5	61	13	26	50	69	37	-0.159237
A	2000-01-14T00:00:00	54	5	9	77	82	61	57	19	96	96	64	2	50.5	1	52	54	45	96	74.5	61	13	26	50	69	37	0.05199
A	2000-01-21T00:00:00	54	5	17	77	82	55	50	48	93	93	64	2	50.5	1	52	54	45	96	74.5	54	13	26	50	69	37	0.005411
A	2000-01-28T00:00:00	54	6	25	43	82	67	74	29	91	91	64	2	50.5	4	52	54	45	97	74.5	73	13	26	50	69	37	-0.010026
A	2000-02-04T00:00:00	53	5	5	43	82	65	71	11	89	89	64	2	50.5	39	52	54	45	97	74.5	70	13	26	50	69	37	0.12033
A	2000-02-11T00:00:00	53	5	4	43	82	68	76	35	87	87	64	2	50.5	45	52	54	46	97	74.5	75	13	26	50	69	37	-0.011402
A	2000-02-18T00:00:00	52	4	21	43	82	76	87	7	88	88	64	2	50.5	9	52	54	45	97	74.5	84	13	26	50	69	37	0.243701
A	2000-02-25T00:00:00	52	4	14	43	82	95	100	5	86	86	64	2	50.5	18	52	54	45	96	74.5	98	13	26	50	69	37	0.153056
A	2000-03-03T00:00:00	52	4	4	43	82	85	91	24	83	83	64	2	50.5	44	52	54	45	97	74.5	91	13	26	50	69	37	-0.000924
A	2000-03-10T00:00:00	51	3	8	43	82	82	87	4	81	81	64	1	50.5	29	52	54	45	96	74.5	87	13	26	50	69	37	0.314813
A	2000-03-17T00:00:00	46	4	16	85	82	76	76	50	82	82	64	2	50.5	89	52	54	40	95	74.5	79	13	26	50	69	37	-0.147183
A	2000-03-24T00:00:00	46	4	34	84	82	77	76	42	82	82	64	2	50.5	89	52	54	40	95	74.5	80	13	26	50	69	37	-0.009086
A	2000-03-31T00:00:00	48	4	29	83	82	82	89	79	83	83	64	2	50.5	89	52	54	40	95	74.5	88	13	26	50	69	37	-0.13333
A	2000-04-07T00:00:00	47	4	34	83	82	76	71	10	83	83	64	2	50.5	89	52	54	40	94	74.5	76	13	26	50	69	37	0.173079
A	2000-04-14T00:00:00	48	4	27	82	82	88	92	78	87	87	64	2	50.5	89	52	54	40	95	74.5	92	13	26	50	69	37	-0.322221
A	2000-04-21T00:00:00	48	4	25	82	82	91	95	69	84	84	53	2	50.5	90	52	54	40	95	74.5	94	13	26	50	69	37	0.087696
A	2000-04-28T00:00:00	48	5	20	82	82	84	86	80	82	82	54	2	50.5	90	52	54	40	95	74.5	86	13	26	50	69	37	-0.014581
A	2000-05-05T00:00:00	48	4	25	82	82	93	97	42	79	79	57	2	50.5	90	52	54	41	94	74.5	96	13	26	50	69	37	0.031719
A	2000-05-12T00:00:00	47	4	33	84	82	90	90	48	79	79	67	2	50.5	89	52	54	41	95	74.5	93	13	26	50	69	37	-0.009633
A	2000-05-19T00:00:00	49	6	30	86	82	89	84	98	82	82	63	2	50.5	89	52	54	42	97	74.5	91	13	26	50	69	37	-0.264233
A	2000-05-26T00:00:00	49	5	36	86	82	78	49	82	80	80	78	2	50.5	88	52	54	42	96	74.5	67	13	26	50	69	37	-0.024474
A	2000-06-02T00:00:00	48	5	20	86	82	96	91	30	83	83	38	2	50.5	89	52	54	42	96	74.5	97	13	26	50	69	37	0.257683
A	2000-06-09T00:00:00	49	6	21	87	82	41	31	85	82	82	40	2	50.5	89	52	54	42	97	74.5	32	13	26	50	69	37	-0.132966
A	2000-06-16T00:00:00	55	7	26	40	82	55	35	96	81	81	80	2	50.5	98	52	54	44	73	74.5	43	13	26	50	69	37	-0.116398
A	2000-06-23T00:00:00	54	5	29	40	82	47	33	31	84	84	80	2	50.5	98	52	54	44	72	74.5	38	13	26	50	69	37	0.199597
A	2000-06-30T00:00:00	54	6	29	40	82	57	40	33	83	83	73	2	50.5	98	52	54	44	72	74.5	47	13	26	50	69	37	-0.018371
A	2000-07-07T00:00:00	55	7	15	40	82	58	53	75	83	83	39	2	50.5	98	52	54	45	73	74.5	56	13	26	50	69	37	-0.084739
A	2000-07-14T00:00:00	54	6	20	40	82	14	10	27	83	83	50	2	50.5	98	52	54	45	72	74.5	9	13	26	50	69	37	0.14163
A	2000-07-21T00:00:00	57	10	16	40	82	19	18	100	93	93	40	3	50.5	98	52	54	45	76	74.5	16	13	26	50	69	37	-0.376323
A	2000-07-28T00:00:00	58	11	16	40	16	42	54	97	91	91	41	3	50.5	38	52	54	45	76	74.5	47	27	34	18	69	37	-0.141718
A	2000-08-04T00:00:00	58	11	32	40	17	17	8	95	90	90	75	3	50.5	39	52	54	45	76	74.5	10	28	33	17	69	37	-0.028856
A	2000-08-11T00:00:00	58	11	19	39	89	13	6	80	89	89	47	3	50.5	40	52	54	45	76	74.5	7	29	34	19	69	37	0.014264
A	2000-08-18T00:00:00	55	7	22	38	84	37	50	9	96	96	91	3	50.5	43	52	54	45	73	74.5	42	25	31	33	69	37	0.387613
A	2000-08-25T00:00:00	55	7	24	38	80	32	52	17	95	95	92	3	50.5	43	52	54	45	73	74.5	41	25	30	32	69	37	0.037607
A	2000-09-01T00:00:00	51	8	22	56	85	19	19	9	93	93	50	3	50.5	43	52	54	49	98	74.5	16	25	31	24	69	37	0.057767
A	2000-09-08T00:00:00	52	8	23	56	83	38	61	42	92	92	52	3	50.5	43	52	54	50	98	74.5	50	21	45	60	69	37	-0.042494
A	2000-09-15T00:00:00	52	9	24	55	82	5	1	60	91	91	54	3	50.5	43	52	54	50	98	74.5	2	35	38	53	69	37	-0.037968
A	2000-09-22T00:00:00	53	10	23	55	84	5	2	80	91	91	51	3	50.5	43	52	54	50	98	74.5	2	34	36	53	69	37	-0.122824
A	2000-09-29T00:00:00	53	10	30	55	67	14	2	79	86	86	66	3	50.5	41	52	54	50	98	74.5	6	36	36	55	69	37	-0.021187
A	2000-10-06T00:00:00	52	9	26	55	76	13	5	28	82	82	59	3	50.5	42	52	54	50	98	74.5	7	36	47	61	69	37	0.066408
A	2000-10-13T00:00:00	53	11	33	55	54	33	62	80	83	83	65	3	50.5	41	52	54	50	98	74.5	47	26	42	61	69	37	-0.149642
A	2000-10-20T00:00:00	53	10	22	55	84	36	62	51	81	81	52	3	50.5	41	52	54	50	98	74.5	48	28	41	65	69	37	0.049104
A	2000-10-27T00:00:00	53	10	25	55	79	40	84	67	65	65	53	3	50.5	41	52	54	50	98	74.5	65	24	30	79	69	37	-0.017372
A	2000-11-03T00:00:00	53	10	27	54	74	24	74	49	61	61	54	3	50.5	41	52	54	50	98	74.5	49	26	34	80	69	37	0.023154
A	2000-11-10T00:00:00	54	12	21	54	84	25	50	89	64	64	51	4	50.5	41	52	54	50	98	74.5	35	24	36	67	69	37	-0.157433
A	2000-11-17T00:00:00	53	9	20	52	85	18	30	15	76	76	10	3	50.5	57	63	54	50	98	74.5	21	33	65	85	69	37	0.201295
A	2000-11-24T00:00:00	52	8	33	52	52	19	22	3	75	75	33	3	50.5	57	74	54	50	98	74.5	19	36	74	78	69	37	0.075161
A	2000-12-01T00:00:00	52	7	25	52	76	31	52	4	70	70	19	3	50.5	55	70	54	50	97	74.5	40	32	72	72	69	37	0.040433
A	2000-12-08T00:00:00	51	6	23	52	80	21	22	3	69	69	16	3	50.5	55	62	54	50	97	74.5	20	26	82	74	69	37	0.121498
A	2000-12-15T00:00:00	51	7	14	53	88	26	33	21	67	67	1	3	50.5	55	71	54	50	97	74.5	28	19	92	73	69	37	-0.043053
A	2000-12-22T00:00:00	51	7	25	53	74	24	32	39	62	62	20	3	50.5	55	38	54	50	97	74.5	26	31	89	70	69	37	-0.038514
A	2000-12-29T00:00:00	51	7	32	52	52	22	40	56	59	59	32	3	50.5	55	25	54	50	97	74.5	28	29	90	71	69	37	0.001108
A	2001-01-05T00:00:00	51	7	22	52	81	17	21	55	55	55	13	3	50.5	54	26	54	50	97	74.5	17	28	88	66	69	37	0.005652
A	2001-01-12T00:00:00	52	7	18	52	86	32	77	59	52	52	6	3	50.5	51	25	54	50	97	74.5	54	31	88	68	69	37	0.019418
A	2001-01-19T00:00:00	71	7	25	88	73	46	97	18	59	59	60	3	50.5	42	5	54	49	96	74.5	73	27	89	62	69	37	0.168018
A	2001-01-26T00:00:00	74	9	25	88	75	57	93	96	63	63	59	3	50.5	41	14	54	49	97	74.5	77	33	89	68	69	37	-0.166725
A	2001-02-02T00:00:00	76	10	16	88	87	73	97	94	63	63	71	3	50.5	42	18	54	49	97	74.5	88	34	90	67	69	37	-0.044476
A	2001-02-09T00:00:00	75	9	28	87	68	66	92	77	62	62	93	3	50.5	41	10	54	49	97	74.5	81	42	92	71	69	37	0.00574
A	2001-02-16T00:00:00	77	10	29	88	64	61	77	83	60	60	95	3	50.5	63	14	54	49	97	74.5	69	50	86	64	69	37	-0.047622
A	2001-02-23T00:00:00	83	14	32	87	54	56	63	97	67	67	100	4	50.5	62	24	54	48	97	74.5	60	47	85	58	69	37	-0.234004
A	2001-03-02T00:00:00	83	15	15	87	89	57	63	87	66	66	66	3	50.5	65	28	54	49	97	74.5	61	42	76	66	69	37	-0.007011
A	2001-03-09T00:00:00	84	15	17	87	88	52	56	84	66	66	72	4	50.5	65	23	54	49	97	74.5	55	42	74	67	69	37	-0.025788
A	2001-03-16T00:00:00	85	15	29	87	67	77	87	70	64	64	95	4	50.5	63	28	54	49	97	74.5	91	45	70	62	69	37	-0.062088
A	2001-03-23T00:00:00	53	14	30	25	78	40	18	22	68	68	62	3	50.5	54	25	54	68	73	74.5	21	44	76	59	69	37	0.088626
A	2001-03-30T00:00:00	57	21	36	25	59	31	12	91	68	68	75	4	50.5	57	43	54	68	74	74.5	15	24	83	62	69	37	-0.187665
A	2001-04-06T00:00:00	59	24	31	25	78	35	22	87	66	66	68	4	50.5	58	48	54	67	74	74.5	24	21	83	61	69	37	-0.09537
A	2001-04-13T00:00:00	56	17	25	25	88	32	20	30	74	74	52	4	50.5	55	38	54	67	73	74.5	23	17	80	55	69	37	0.221627
A	2001-04-20T00:00:00	56	14	33	25	72	20	10	6	76	76	71	3	50.5	50	38	54	67	73	74.5	13	22	78	59	69	37	0.189615
A	2001-04-27T00:00:00	56	15	30	25	82	20	11	33	75	75	68	4	50.5	50	37	54	67	73	74.5	14	33	93	47	69	37	-0.039838
A	2001-05-04T00:00:00	56	16	32	24	76	20	13	61	71	71	71	4	50.5	57	39	54	67	74	74.5	15	35	94	51	69	37	-0.010848
A	2001-05-11T00:00:00	56	15	35	25	68	22	18	35	70	70	76	4	50.5	58	44	54	67	73	74.5	18	34	93	48	69	37	0.034393
A	2001-05-18T00:00:00	60	20	23	30	89	27	43	86	70	70	45	4	50.5	62	75	54	67	74	74.5	33	37	94	39	69	37	-0.092942
A	2001-05-25T00:00:00	59	18	27	30	87	21	11	55	69	69	55	4	50.5	62	56	54	67	74	74.5	14	34	95	38	69	37	0.046935
A	2001-06-01T00:00:00	61	21	25	30	88	21	13	94	70	70	48	4	50.5	62	59	54	67	75	74.5	15	36	95	42	69	37	-0.098697
A	2001-06-08T00:00:00	61	20	35	30	71	27	44	69	67	67	80	4	50.5	62	71	54	67	75	74.5	32	35	95	41	69	37	0.030605
A	2001-06-15T00:00:00	52	25	28	56	86	38	82	92	69	69	35	4	50.5	69	74	54	68	98	74.5	62	34	90	34	69	37	-0.129963
A	2001-06-22T00:00:00	53	26	23	57	90	28	69	84	65	65	2	4	50.5	70	66	54	67	98	74.5	47	55	92	33	69	37	-0.034777
A	2001-06-29T00:00:00	52	24	23	56	90	27	45	41	64	64	1	4	50.5	70	51	54	67	98	74.5	31	52	94	30	69	37	0.105419
A	2001-07-06T00:00:00	53	24	41	56	60	30	66	74	63	63	33	4	50.5	65	55	54	68	98	74.5	47	52	95	29	69	37	-0.072292
A	2001-07-13T00:00:00	53	25	34	56	77	22	21	59	62	62	19	4	50.5	67	60	54	67	98	74.5	17	52	95	29	69	37	0.004942
A	2001-07-20T00:00:00	53	27	31	56	83	22	30	81	61	61	15	4	50.5	64	92	54	67	99	74.5	21	53	94	44	69	37	-0.046201
A	2001-07-27T00:00:00	53	26	31	56	83	19	9	35	60	60	15	4	50.5	63	79	54	67	99	74.5	11	41	86	7	69	37	0.041527
A	2001-08-03T00:00:00	52	24	28	56	87	18	16	22	59	59	9	4	50.5	65	74	54	68	99	74.5	14	34	82	12	69	37	0.039872
A	2001-08-10T00:00:00	53	28	29	54	86	15	9	77	59	59	10	4	50.5	66	80	54	68	98	74.5	10	36	84	18	69	37	-0.086572
A	2001-08-17T00:00:00	54	30	34	51	75	16	12	87	59	59	19	5	50.5	68	66	54	68	98	74.5	12	45	78	13	69	37	-0.074848
A	2001-08-24T00:00:00	53	30	30	51	84	19	35	57	58	58	11	4	50.5	69	52	54	68	98	74.5	24	39	78	14	69	37	0.050634
A	2001-08-31T00:00:00	53	29	36	51	72	18	19	74	58	58	24	5	50.5	67	40	54	68	98	74.5	17	37	78	14	69	37	-0.046427
A	2001-09-07T00:00:00	55	37	36	51	71	19	29	86	59	59	26	5	50.5	67	48	54	68	98	74.5	21	35	78	14	69	37	-0.118876
A	2001-09-14T00:00:00	27	37	32	86	79	19	23	86	58	58	16	5	50.5	40	49	54	66	98	74.5	19	45	85	20	69	37	-0.012832
A	2001-09-21T00:00:00	27	39	29	86	83	19	40	64	50	50	10	5	50.5	47	65	54	66	98	74.5	26	53	84	53	69	37	-0.125799
A	2001-09-28T00:00:00	26	41	36	86	71	21	62	79	45	45	24	5	50.5	41	56	54	66	98	74.5	39	60	82	57	69	37	-0.029739
A	2001-10-05T00:00:00	27	40	27	85	85	18	23	46	48	48	9	5	50.5	49	43	54	66	98	74.5	18	47	73	52	69	37	0.079223
A	2001-10-12T00:00:00	27	34	38	86	65	16	17	20	51	51	34	5	50.5	38	46	54	66	98	74.5	15	46	72	53	69	37	0.107162
A	2001-10-19T00:00:00	27	34	35	85	73	18	28	22	49	49	35	5	50.5	40	42	54	66	98	74.5	20	45	73	52	69	37	0

End of preview. Expand in Dataset Viewer.

Factor Signals

Data Notice: This dataset provides academic research access with a 6-month data lag. For real-time data access, please visit sov.ai to subscribe. For market insights and additional subscription options, check out our newsletter at blog.sov.ai.

from datasets import load_dataset
df_factor_comp = load_dataset("sovai/factor_signals", split="train").to_pandas().set_index(["ticker","date"])

Data is updated weekly as data arrives after market close US-EST time.

Tutorials are the best documentation — Factor Signals Tutorial

Category	Details
Input Datasets	Filings, Financial Data
Models Used	OLS Regression
Model Outputs	Factors, Coefficients, Standard Errors

Description

This dataset includes traditional accounting factors, alternative financial metrics, and advanced statistical analyses, enabling sophisticated financial modeling.

It could be used for bottom-up equity selection strategies and for the development of investment strategies.

Data Access

Comprehensive Factors

Comprehensive Factors dataset is a merged set of both accounting and alternative financial metrics, providing a holistic view of a company's financial status.

import sovai as sov
df_factor_comp = sov.data("factors/comprehensive",tickers=["MSFT","TSLA"])

Accounting Factors

The Accounting Factors dataset includes key financial metrics related to accounting for various companies.

import sovai as sov
df_factor_actn = sov.data("factors/accounting",tickers=["MSFT","TSLA"])

Alternative Factors

This dataset contains alternative financial factors that are not typically found in standard financial statements.

import sovai as sov
df_factor_alt = sov.data("factors/alternative",tickers=["MSFT","TSLA"])

Coefficients Factors

The Coefficients Factors dataset includes various coefficients related to different financial metrics.

import sovai as sov
df_factor_coeff = sov.data("factors/coefficients",tickers=["MSFT","TSLA"])

Standard Errors Factors

This dataset provides standard errors for various financial metrics, useful for statistical analysis and modeling.

import sovai as sov
df_factor_std_err = get_data("factors/standard_errors",tickers=["MSFT","TSLA"])

T-Statistics Factors

The T-Statistics Factors dataset includes t-statistics for different financial metrics, offering insights into their significance.

import sovai as sov
df_factor_t_stat = get_data("factors/t_statistics",tickers=["MSFT","TSLA"])

Model Metrics

Model Metrics dataset includes various metrics such as R-squared, AIC, BIC, etc., that are crucial for evaluating the performance of financial models.

import sovai as sov
df_model_metrics = sov.data("factors/model_metrics",tickers=["MSFT","TSLA"])

This documentation provides a clear guide on how to access each dataset, and can be easily extended or modified as needed for additional datasets or details.

Data Dictionary

Financial Factors Dataset

Name	Description
`ticker`	The unique identifier for a publicly traded company's stock.
`date`	The specific date for which the data is recorded.
`profitability`	A measure of a company's efficiency in generating profits.
`value`	Indicates the company's market value, often reflecting its perceived worth.
`solvency`	Reflects the company's ability to meet its long-term financial obligations.
`cash_flow`	Represents the amount of cash being transferred into and out of a business.
`illiquidity`	Measures the difficulty of converting assets into cash quickly without significant loss in value.
`momentum_long_term`	Indicates long-term trends in the company's stock price movements.
`momentum_medium_term`	Represents medium-term trends in stock price movements.
`short_term_reversal`	Reflects short-term price reversals in the stock market.
`price_volatility`	Measures the degree of variation in a company's stock price over time.
`dividend_yield`	The dividend per share, divided by the price per share, showing how much a company pays out in dividends each year relative to its stock price.
`earnings_consistency`	Indicates the stability and predictability of a company's earnings over time.
`small_size`	A factor indicating the company's size, with smaller companies potentially offering higher returns (albeit with higher risk).
`low_growth`	Reflects the company's lower-than-average growth prospects.
`low_equity_issuance`	Indicates a lower level of issuing new shares, which can be a sign of financial strength or limited growth prospects.
`bounce_dip`	Measures the tendency of a stock to recover quickly after a significant drop.
`accrual_growth`	Represents the growth rate in accruals, which are earnings not yet realized in cash.
`low_depreciation_growth`	Indicates lower growth in depreciation expenses, which might suggest more stable capital expenditures.
`current_liquidity`	A measure of a company's ability to pay off its short-term liabilities with its short-term assets.
`low_rnd`	Reflects lower expenditures on research and development, which could indicate less investment in future growth.
`momentum`	Overall momentum factor, representing the general trend in the stock price movements.
`market_risk`	Indicates the risk of an investment in a particular market relative to the entire market.
`business_risk`	Reflects the inherent risk associated with the specific business activities of a company.
`political_risk`	Measures the potential for losses due to political instability or changes in a country's political environment.
`inflation_fluctuation`	Indicates how sensitive the company is to fluctuations in inflation rates.
`inflation_persistence`	Measures the company's exposure to persistent inflation trends.
`returns`	Represents the financial returns generated by the company over a specified period.

ModelMetrics Dataset

Name	Description
`ticker`	The unique stock ticker symbol identifying the company.
`date`	The date for which the model metrics are calculated.
`rsquared`	The R-squared value, indicating the proportion of variance in the dependent variable that's predictable from the independent variables.
`rsquared_adj`	The adjusted R-squared value, accounting for the number of predictors in the model (provides a more accurate measure when dealing with multiple predictors).
`fvalue`	The F-statistic value, used to determine if the overall regression model is a good fit for the data.
`aic`	Akaike’s Information Criterion, a measure of the relative quality of statistical models for a given set of data. Lower AIC indicates a better model.
`bic`	Bayesian Information Criterion, similar to AIC but with a higher penalty for models with more parameters.
`mse_resid`	Mean Squared Error of the residuals, measuring the average of the squares of the errors, i.e., the average squared difference between the estimated values and the actual value.
`mse_total`	Total Mean Squared Error, measuring the total variance in the observed data.

In addition to the primary financial metrics and model metrics, our data suite includes three specialized datasets:

Coefficients: This dataset provides regression coefficients for various financial factors. These coefficients offer insights into the relative importance and impact of each factor in financial models.
Standard Errors: Accompanying the coefficients, this dataset provides the standard error for each coefficient. The standard errors are crucial for understanding the precision and reliability of the coefficients in the model.
T-Statistics: This dataset contains the t-statistic for each coefficient, a key metric for determining the statistical significance of each financial factor. It helps in evaluating the robustness of the coefficients' impact in the model.

These datasets form a comprehensive toolkit for financial analysis, enabling detailed regression analysis and statistical evaluation of financial factors.

Factor Analysis Datasets

Our suite of Factor Analysis datasets offers a rich and comprehensive resource for investors seeking to deepen their understanding of market dynamics and enhance their investment strategies. Here's an overview of each dataset and its potential use cases:

Comprehensive Financial Metrics

Accounting Factors (FactorsAccounting): This dataset includes core financial metrics like profitability, solvency, and cash flow. It's invaluable for fundamental analysis, enabling investors to assess a company's financial health and operational efficiency.
Alternative Factors (FactorsAlternative): Focusing on non-traditional financial metrics such as market risk, business risk, and political risk, this dataset helps in evaluating external factors that could impact a company's performance.
Comprehensive Factors (FactorsComprehensive): A merged set of accounting and alternative factors providing a holistic view of a company's status. This dataset is perfect for a comprehensive financial analysis, blending traditional and modern financial metrics.

Advanced Statistical Analysis

Coefficients (FactorsCoefficients): Reveals the weight or importance of each financial factor in a statistical model. Investors can use this to identify which factors are most influential in predicting stock performance.
Standard Errors (FactorsStandardErrors): Provides precision levels of the coefficients. This is crucial for investors in assessing the reliability of the coefficients in predictive models.
T-Statistics (FactorsTStatistics): Offers insights into the statistical significance of each factor. Investors can use this to gauge the robustness and credibility of the factors in their investment models.
Model Metrics (ModelMetrics): Includes advanced metrics like R-squared, AIC, and BIC. This dataset is essential for evaluating the effectiveness of financial models, helping investors to choose the most reliable models for their investment decisions.

Potential Use Cases

Portfolio Construction and Optimization: By understanding the importance and impact of various financial factors, investors can construct and optimize their portfolios to maximize returns and minimize risks.
Risk Assessment and Management: Alternative factors, along with risk-related metrics from other datasets, enable investors to conduct thorough risk assessments, leading to better risk management strategies.
Market Trend Analysis: Long-term and medium-term momentum factors can be used for identifying prevailing market trends, aiding in strategic investment decisions.
Statistical Model Validation: Investors can validate their financial models using model metrics and statistical datasets (Standard Errors and T-Statistics), ensuring robustness and reliability in their analysis.

Downloads last month: 36

Edit dataset card

Size of downloaded dataset files:

213 MB

Size of the auto-converted Parquet files:

213 MB

Number of rows:

8,974,819