3 weeks, 1 day ago

FedCSIS 2024 Data Science Challenge: Predicting Stock Trends

FedCSIS 2024 Data Science Challenge: Predicting Stock Trends is the 10th data science challenge organized under the auspices of the Conference on Computer Science and Intelligence Systems (https://fedcsis.org/). In this anniversary edition, the task is related to financial data - participants are asked to predict the performance of investments in selected stocks from several industry sectors. The competition is sponsored by Yettel.Bank (former Mobi Banka) (https://www.yettelbank.rs/en/) and the Conference on Computer Science and Intelligence Systems series.

The topic of this year's data science competition is the prediction of stock trends. The dataset contains key financial indicators for 300 companies chosen from 11 different sectors of the S&P 500 index, from 10 years. Each company is described by values of 58 indicators that are derived from its financial statements. The dataset also contains information on 1-year change for each indicator, which can indicate a trend in the considered values. The task for the competition participants is to develop a predictive model able to accurately forecast stock trend movements based on the provided financial fundamental data. Such a model could have a vital role in algorithmic or manual trading, providing the trading signals for making decisions about whether it is a good moment to buy or sell the stock, or maybe not to trade at all. The competition's sponsor is Yettel.Bank (part of the PPF group, former Mobi Banka) – the first bank in the Western Balkan region where banking is done fully mobile.

Special session at FedCSIS 2024: As in previous years, a special session devoted to the competition will be held at the conference. We will invite authors of selected challenge reports to extend them for publication in the conference proceedings (after reviews by Organizing Committee members) and presentation at the conference. The papers will be indexed by the IEEE Digital Library and Web of Science. The invited teams will be chosen based on their final rank, the innovativeness of their approach, and the quality of the submitted report.

Terms & Conditions
 
 
Rank Team Name Is Report Preliminary Score Final Score Submissions
1
NxGTR
True 0.6584 0.772000 52
2
hieuvq
True 0.7376 0.800300 220
3
StockTrends
True 0.7228 0.802000 52
4
beamon
True 0.6980 0.805900 90
5
Pattern Pioneers
True 0.7822 0.807600 51
6
Team1
True 0.7525 0.809800 20
7
Stokastik Heinz
True 0.7970 0.818700 27
8
Data_Bombers
True 0.7624 0.823700 53
9
The Singleton
True 0.7921 0.825900 5
10
No-Name
True 0.7426 0.827000 39
11
TUmany Data
True 0.7030 0.830400 56
12
DataDiggers
True 0.7376 0.833100 66
13
Tsilkow
True 0.8020 0.833100 5
14
DML
True 0.5941 0.841500 401
15
O.W.C.A.
True 0.8020 0.843200 18
16
Amy
True 0.6485 0.845400 240
17
DataCraft
True 0.8069 0.847100 39
18
smatcosk
True 0.7178 0.848700 139
19
Dymitr
True 0.7178 0.849800 168
20
mk
True 0.7772 0.850900 11
21
baseline
True 0.8515 0.854800 5
22
unicas09
True 0.7277 0.857100 26
23
Akash Gupta
True 0.8416 0.858200 3
24
Unicas04
True 0.7376 0.862600 50
25
AIBrain
True 0.6485 0.865400 90
26
unicas14
True 0.7772 0.867600 118
27
l31415
True 0.8168 0.867600 4
28
Adam
True 0.8218 0.869300 5
29
Unicas15
True 0.7921 0.872600 40
30
undefined
True 0.8515 0.881000 3
31
team name
True 0.9950 0.903200 1
32
unicas01
True 0.7079 0.903800 67
33
DataCrusher
False 0.7178 No report file found or report rejected. 28
34
wxyz
False 0.7178 No report file found or report rejected. 40
35
unicas10
False 0.7277 No report file found or report rejected. 70
36
Khakisark
False 0.7327 No report file found or report rejected. 25
37
Cyan
False 0.7475 No report file found or report rejected. 54
38
Cena
False 0.7475 No report file found or report rejected. 5
39
unicas11
False 0.7475 No report file found or report rejected. 54
40
Hawk
False 0.7525 No report file found or report rejected. 14
41
unicas06
False 0.7525 No report file found or report rejected. 42
42
basakesin
False 0.7574 No report file found or report rejected. 11
43
unicas21
False 0.7574 No report file found or report rejected. 59
44
unicas18
False 0.7624 No report file found or report rejected. 13
45
unicas05
False 0.7475 No report file found or report rejected. 106
46
Hackafon
False 0.7723 No report file found or report rejected. 21
47
Statistik
False 0.7723 No report file found or report rejected. 3
48
Beta
False 0.7723 No report file found or report rejected. 2
49
rdeggau
False 0.7772 No report file found or report rejected. 38
50
recreativo
False 0.7772 No report file found or report rejected. 11
51
wdrz
False 0.7673 No report file found or report rejected. 7
52
TUmuch Data
False 0.7772 No report file found or report rejected. 4
53
unicas13
False 0.7772 No report file found or report rejected. 13
54
CUFE
False 0.7871 No report file found or report rejected. 13
55
VNteam
False 0.7871 No report file found or report rejected. 16
56
Unity
False 0.7970 No report file found or report rejected. 8
57
UseUrBrain
False 0.8020 No report file found or report rejected. 11
58
mya
False 0.8069 No report file found or report rejected. 1
59
unicas07
False 0.8069 No report file found or report rejected. 33
60
tch
False 0.7673 No report file found or report rejected. 4
61
Felix
False 0.8119 No report file found or report rejected. 5
62
1
False 0.8119 No report file found or report rejected. 20
63
IML-MIMUW-0
False 0.8218 No report file found or report rejected. 2
64
unicas
False 0.8218 No report file found or report rejected. 39
65
capybara
False 0.8267 No report file found or report rejected. 5
66
SUDHIR KUMAR JOON
False 0.8267 No report file found or report rejected. 23
67
buraczki
False 0.8267 No report file found or report rejected. 2
68
deterministicforest
False 0.8366 No report file found or report rejected. 1
69
sy-pl
False 0.8515 No report file found or report rejected. 7
70
kubapok
False 0.8564 No report file found or report rejected. 5
71
418 teapots
False 0.8564 No report file found or report rejected. 3
72
Mungosi
False 0.8564 No report file found or report rejected. 2
73
neil
False 0.8911 No report file found or report rejected. 1
74
Juul
False 0.9010 No report file found or report rejected. 5
75
mmoczulski
False 0.9406 No report file found or report rejected. 1
76
idcatc
False 0.9653 No report file found or report rejected. 1
77
ANickeN
False 1.0396 No report file found or report rejected. 1

The task in this challenge is to design an accurate method for predicting a trading action (buy, sell, hold). The available training data contains 8,000 instances with fundamental financial data in a tabular format (CSV file with semicolons used as separators). Each instance in data represents an event – a financial statement announcement for one of the chosen 300 companies. It contains information on the company’s sector, values for 58 key financial indicators, 1-year (absolute) change for each of the 58 indicators, target class information (the column 'Class'), and risk-return performance for a period after the announcement (the column 'Perform').

Please note that the data contains two distinct types of missing values that have different semantics. One corresponds to non-available/missing information and another one can be interpreted as non-applicable. In data, one type is marked by "NA" string and another is just an empty string (there is no value).

Solution format: the test data, containing 2,000 instances, is also provided as a CSV file. The test file has the same format and naming scheme as the training data but it does not contain columns 'Class' and 'Perform'.

Solutions in this competition should be submitted to the online evaluation system as a text file with exactly 2,000 lines containing predictions for test instances. Each line in the submission should contain a single number from the set {1, 0, -1} that indicates the predicted trading action for the event. The ordering of predictions should be the same as the ordering of the test set.

Evaluation: the quality of submissions will be evaluated using the average error cost measure with the error cost matrix given below:

  -1 0 1
-1 0 1 2
0 1 0 1
1 2 1 0

 

In particular, the error value is computed as: err = (confusion_matrix(preds, gt) * cost_matrix)/length(gt)), where the multiplication is done element-wise.

Solutions will be evaluated online, and the preliminary results will be published on the public leaderboard. The preliminary score will be computed on a small subset of the test records, fixed for all participants. The final evaluation will be performed after the completion of the competition using the remaining part of the test records. Those results will also be published online. It is important to note that only teams that submit a report describing their approach before the end of the challenge will qualify for the final evaluation.

In order to download competition files you need to be enrolled.
  • March 1, 2024: start of the competition
  • May 10 May 24, 2024 (23:59 GMT): deadline for submitting the predictions
  • May 12 May 26, 2024 (23:59 GMT): deadline for sending the reports, end of the competition
  • May 19 June 2, 2024: online publication of the final results, sending invitations for submitting short papers for the special session at FedCSIS'24
  • June 16 June 30, 2024: deadline for submitting invited papers
  • June 30 July 14, 2024: notification of paper acceptance
  • July 9 July 23, 2024: camera-ready of accepted papers, and registration for the conference are due

Authors of the top-ranked solutions (based on the final evaluation scores) will be awarded prizes funded by the Sponsors:

  •     1000 EUR for the winning solution + one free FedCSIS 2024 registration
  •     600 EUR for the 2nd place solution + one free FedCSIS 2024 registration
  •     300 EUR for the 3rd place solution + one free FedCSIS 2024 registration
  •     special award of 300 EUR for the solution with the highest cummulative performance + one free FedCSIS 2024 registration
  • Aleksandar M. Rakićević, University of Belgrade
  • Pavle D. Milošević, University of Belgrade
  • Ivana T. Dragović, University of Belgrade
  • Ana M. Poledica, University of Belgrade
  • Milica M. Zukanović, University of Belgrade
  • Ivan S. Luković, University of Belgrade
  • Andrzej Janusz, Queensland University of Technology / QED Software
  • Dominik Ślęzak, University of Warsaw / QED Software

The main sponsor of the competition, Yettel.Bank (former Mobi Banka), is a part of the PPF group and the first bank in the Western Balkan region where banking is done differently - fully mobile. With its unique user platform for mobile phones and PCs, it offers innovative digital banking services, simple to use and available anytime and anywhere.

This forum is for all users to discuss matters related to the competition. Good manners apply!
  Discussion Author Replies Last post
Final results - Special award winner Aleksandar 2 by Carlos
Saturday, June 15, 2024, 00:09:20
Final results Aleksandar 4 by Andrzej
Thursday, June 06, 2024, 05:45:06
Thank you for participating! Andrzej 2 by M
Thursday, May 30, 2024, 11:14:07
Data Understanding Prachi 1 by Milica
Monday, May 13, 2024, 17:46:25
Deadline extension - May 24, 2024 Aleksandar 0 by Aleksandar
Wednesday, April 10, 2024, 11:05:50
Unable to merge teams 1 by Andrzej
Tuesday, April 02, 2024, 17:53:36
metric code Karol 2 by Dymitr
Wednesday, March 13, 2024, 20:25:33
Time order Dymitr 1 by Aleksandar
Friday, March 08, 2024, 16:30:31