Advances in Predictive Analytics Techniques for Capital Allocation Under Volatile Market Conditions

References

[1] Adrian, T., 2018. Risk management and regulation. International Monetary Fund.

[2] Adrian, T., Fleming, M., Shachar, O. and Vogt, E., 2017. Market liquidity after the financial crisis. Annual Review of Financial Economics, 9(1), pp.43-83.

[3] Ahmed, K.S., Odejobi, O.D. and Oshoba, T.O., 2019. Algorithmic model for constraint satisfaction in cloud network resource allocation. IRE Journals, 2(12), pp.516-532.

[4] Anichukwueze, C.C., Osuji, V.C. and Oguntegbe, E.E., 2019. Global marketing law and consumer protection challenges: a strategic framework for multinational compliance. IRE Journals, 3(6), pp.325-333.

[5] ARANSI, A.N., BAYEROJU, O.F., Queen, Z.A.M.A.T.H.U.L.A. and Nwokediegwu, S.I.K.H.A.K.H.A.N.E., 2019. Circular economy integration in construction: conceptual framework for modular housing adoption.

[6] Badmus, O., & Olamide, A. L. (2018). Data-driven framework for predicting subsurface contamination pathways in complex remediation projects. Iconic Research and Engineering Journals, 2(5), 312–318.

[7] Badmus, O., & Olamide, A. L. (2019). Advanced hydrological modeling approach for assessing climate-induced watershed vulnerability trends. IRE Journals, 3(5), 388–400.

[8] Baruh, L. and Popescu, M., 2017. Big data analytics and the limits of privacy self-management. New media & society, 19(4), pp.579-596.

[9] Bayeroju, O.F., Sanusi, A.N., Queen, Z.A.M.A.T.H.U.L.A. and Nwokediegwu, S.I.K.H.A.K.H.A.N.E., 2019. Bio-based materials for construction: a global review of sustainable infrastructure practices. J Front Multidiscip Res, 1(1), pp.45-56.

[10] Bebchuk, L.A., Cohen, A. and Hirst, S., 2017. The agency problems of institutional investors. Journal of Economic Perspectives, 31(3), pp.89-112.

[11] Campbell, J.Y., 2017. Financial decisions and markets: a course in asset pricing. Princeton University Press.

[12] Celestin, M., 2017. The role of budgeting in startups: How early-stage companies can use lean budgeting techniques to optimize cash flow and minimize financial risks. Brainae Journal of Business, Sciences and Technology (BJBST), 1(19), pp.661-672.

[13] Dako, O.F., Okafor, C.M., Farounbi, B.O. and Onyelucheya, O.P., 2019. Detecting financial statement irregularities: Hybrid Benford–outlier–process-mining anomaly detection architecture. IRE Journals, 3(5), pp.312-327.

[14] Dako, O.F., Onalaja, T.A., Nwachukwu, P.S., Bankole, F.A. and Lateefat, T., 2019. Blockchain-enabled systems fostering transparent corporate governance, reducing corruption, and improving global financial accountability. IRE Journals, 3(3), pp.259-266.

[15] Dako, O.F., Onalaja, T.A., Nwachukwu, P.S., Bankole, F.A. and Lateefat, T., 2019. AI-driven fraud detection enhancing financial auditing efficiency and ensuring improved organizational governance integrity. IRE Journals, 2(11), pp.556-563.

[16] Dako, O.F., Onalaja, T.A., Nwachukwu, P.S., Bankole, F.A. and Lateefat, T., 2019. Business process intelligence for global enterprises: Optimizing vendor relations with analytical dashboards. IRE Journals, 2(8), pp.261-270.

[17] Davis, M.J., Lu, Y., Sharma, M., Squillante, M.S. and Zhang, B., 2018. Stochastic optimization models for workforce planning, operations, and risk management. Service Science, 10(1), pp.40-57.

[18] Dorgbefu, E.A., 2018. Leveraging predictive analytics for real estate marketing to enhance investor decision-making and housing affordability outcomes. Int J Eng Technol Res Manag, 2(12), p.135.

[19] Ekechi, A. T. (2019). Framework for Lifecycle Management and Recycling of Spent Lithium-Ion Battery Components. International Journal of Multidisciplinary Research and Growth Evaluation, 4(6), 1271 - 1290. https://doi.org/10.54660/.IJMRGE.2023.4.6.1271-1290

[20] Farounbi, B.O., Akinola, A.S., Adesanya, O.S. and Okafor, C.M., 2018. Automated payroll compliance assurance: Linking withholding algorithms to financial statement reliability. IRE Journals, 1(7), pp.341-357.

[21] Fays, B., Lambert, M. and Papageorgiou, N., 2018. Looking for the Tangent Portfolio: Risk Optimization Techniques on Equity Style Buckets.

[22] GAFFAR, O., SIKIRU, A.O., OTUNBA, M. and ADENUGA, A.A., 2019. A Predictive Analytics Model for Multi-Currency IT Operational Expenditure Management.

[23] Gao, X. and Nardari, F., 2018. Do commodities add economic value in asset allocation? New evidence from time-varying moments. Journal of Financial and Quantitative Analysis, 53(1), pp.365-393.

[24] Gil-Ozoudeh, I.D.S., Aransi, A.N., Nwafor, M.I. and Uduokhai, D.O., 2018. Socioeconomic determinants influencing the affordability and sustainability of urban housing in Nigeria. IRE Journals, 2(3), pp.164-169.

[25] Hammerschmid, R. and Lohre, H., 2018. Regime shifts and stock return predictability. International Review of Economics & Finance, 56, pp.138-160.

[26] Hoffmann, D., Müller, T. and Ahlemann, F., 2017. Balancing alignment, adaptivity, and effectiveness: design principles for sustainable IT project portfolio management.

[27] Horlach, B., Schirmer, I. and Drews, P., 2019. Agile portfolio management: design goals and principles.

[28] Kyere Yeboah, B., and Enow, O. F. (2018). Conceptual framework for reliability-centered maintenance programs in electricity distribution utilities. Iconic Research and Engineering Journals, 2(3), 140 to 153.

[29] Kyere Yeboah, B., and Enow, O. F. (2019). Policy model for root cause failure analysis integration in high-voltage grid management. Iconic Research and Engineering Journals, 2(12), 549 to 562.

[30] Larson, V.E., 2017. CLUBB-SILHS: A parameterization of subgrid variability in the atmosphere. arXiv preprint arXiv:1711.03675.

[31] Loudon, G., 2017. The impact of global financial market uncertainty on the risk-return relation in the stock markets of G7 countries. Studies in Economics and Finance, 34(1), pp.2-23.

[32] Michael, O.N. and Ogunsola, O.E., 2019. Determinants of access to agribusiness finance and their influence on enterprise growth in rural communities. Iconic Research and Engineering Journals, 2(12), pp.533-548.

[33] Michael, O.N. and Ogunsola, O.E., 2019. Strengthening agribusiness education and entrepreneurial competencies for sustainable youth employment in Sub-Saharan Africa. IRE Journals.

[34] Mullangi, M.K., Yarlagadda, V.K., Dhameliya, N. and Rodriguez, M., 2018. Integrating AI and Reciprocal Symmetry in Financial Management: A Pathway to Enhanced Decision-Making. Int. J. Reciprocal Symmetry Theor. Phys, 5(1), pp.42-52.

[35] NWAFOR, M.I., STEPHEN, G., UDUOKHAI, D.O. and ARANSI, A.N., 2018. Socioeconomic determinants influencing the affordability and sustainability of urban housing in Nigeria. Iconic Research and Engineering Journals, 2(3), pp.154-169.

[36] Nwafor, M.I., Uduokhai, D.O., Ifechukwu, G.O., Stephen, D.E.S.M.O.N.D. and Aransi, A.N., 2019. Developing an analytical framework for enhancing efficiency in public infrastructure delivery systems. Iconic Research and Engineering Journals, 2(11), pp.657-670.

[37] Nwafor, M.I., Uduokhai, D.O., Ifechukwu, G.O., Stephen, D.E.S.M.O.N.D. and Aransi, A.N., 2019. Quantitative evaluation of locally sourced building materials for sustainable low-income housing projects. Iconic Res Eng J, 3(4), pp.568-82.

[38] Nwafor, M.I., Uduokhai, D.O., Ifechukwu, G.O., Stephen, D.E.S.M.O.N.D. and Aransi, A.N., 2018. Impact of climatic variables on the optimization of building envelope design in humid regions. Iconic Res Eng J, 1(10), pp.322-35.

[39] Nwaimo, C.S., Oluoha, O.M. and Oyedokun, O.Y.E.W.A.L.E., 2019. Big data analytics: technologies, applications, and future prospects. Iconic Research and Engineering Journals, 2(11), pp.411-419.

[40] Odejobi, O.D. and Ahmed, K.S., 2018. Performance evaluation model for multi-tenant Microsoft 365 deployments under high concurrency. IRE Journals, 1(11), pp.92-107.

[41] Odejobi, O.D. and Ahmed, K.S., 2018. Statistical model for estimating daily solar radiation for renewable energy planning. IRE Journals, 2(5), pp.1-12.

[42] Odejobi, O.D., Hammed, N.I. and Ahmed, K.S., 2019. Approximation complexity model for cloud-based database optimization problems. IRE Journals, 2(9), pp.1-10.

[43] Oguntegbe, E.E., Farounbi, B.O. and Okafor, C.M., 2019. Conceptual model for innovative debt structuring to enhance midmarket corporate growth stability. IRE Journals, 2(12), pp.451-463.

[44] Oguntegbe, E.E., Farounbi, B.O. and Okafor, C.M., 2019. Empirical review of risk-adjusted return metrics in private credit investment portfolios. IRE Journals, 3(4), pp.494-505.

[45] Okeke, O. T., Ugwu-Oju, U. M., & Nwankwo, C. O. (2019). Advances in operating system integration improving productivity in business environments. IRE Journals, 2(9), 432–441.

[46] Okeke, O. T., Ugwu-Oju, U. M., & Nwankwo, C. O. (2019). Conceptual model improving troubleshooting performance in enterprise information technology support. IRE Journals, 3(1), 614–622.

[47] Olamide, A. L., & Badmus, O. (2018). Spatially explicit risk modeling framework for tracking subsurface contaminant migration in data-limited remediation sites. IRE Journals, 2(6), 178–189.

[48] Olamide, A. L., & Badmus, O. (2019). Climate-responsive groundwater vulnerability assessment model integrating hydrological variability and land-use change. IRE Journals, 3(6), 449–460.

[49] Olayinka, O.H., 2019. Leveraging predictive analytics and machine learning for strategic business decision-making and competitive advantage. International Journal of Computer Applications Technology and Research, 8(12), pp.473-486.

[50] Omopariola, B.J. and Aboaba, V., 2019. Comparative analysis of financial models: Assessing efficiency, risk, and sustainability. Int J Comput Appl Technol Res, 8(5), pp.217-231.

[51] Oshoba, T.O., Hammed, N.I. and Odejobi, O.D., 2019. Secure identity and access management model for distributed and federated systems. IRE Journals, 3(4), pp.550-567.

[52] Roundy, P.T. and Bayer, M.A., 2019. To bridge or buffer? A resource dependence theory of nascent entrepreneurial ecosystems. Journal of Entrepreneurship in Emerging Economies, 11(4), pp.550-575.

[53] Seyi-Lande, O.B., Oziri, S.T. and Arowogbadamu, A.A.G., 2018. Leveraging business intelligence as a catalyst for strategic decision-making in emerging telecommunications markets. Iconic Research and Engineering Journals, 2(3), pp.92-105.

[54] Shobande, A.O., Atere, D.E.B.O.R.A.H. and Toluwase, I.H., 2019. Conceptual Model for Evaluating Mid-Market M&A Transactions Using Risk-Adjusted Discounted Cash Flow Analysis. IRE Journals, 2(7), pp.241-247.

[55] Su, Z., Fang, T. and Yin, L., 2019. Understanding stock market volatility: What is the role of US uncertainty?. The North American Journal of Economics and Finance, 48, pp.582-590.

[56] Subramanian, A.S.R., Gundersen, T. and Adams, T.A., 2018. Modeling and simulation of energy systems: A review. Processes, 6(12), p.238.

[57] Ugwu-Oju, U. M., Okeke, O. T., & Nwankwo, C. O. (2018). Advances in cybersecurity protection for sensitive business digital infrastructure. IRE Journals, 1(11), 127–135.

[58] Ugwu-Oju, U. M., Okeke, O. T., & Nwankwo, C. O. (2018). Conceptual model improving encryption strategies for organizational information protection. IRE Journals, 2(2), 139–147.

[59] Ugwu-Oju, U. M., Okeke, O. T., & Nwankwo, C. O. (2018). Conceptual model improving digital workflows within organizational information technology operations. IRE Journals, 2(5), 294–302.

[60] Ugwu-Oju, U. M., Okeke, O. T., & Nwankwo, C. O. (2018). Review of network protocol stability techniques for enterprise information systems. IRE Journals, 1(8), 196–204.

[61] Yeboah, B. K., & Enow, O. F. (2018, September 30). Conceptual framework for reliability-centered maintenance programs in electricity distribution utilities. Iconic Research and Engineering Journals, 2(3), 140–153.

@article{1714304,
      author = {Elikem Kwasi Agbosu, Lovelyn Ekpedo, Omolara Adeyoyin},
      title = {Advances in Predictive Analytics Techniques for Capital Allocation Under Volatile Market Conditions},
      journal = {Iconic Research And Engineering Journals},
      year = {2020},
      volume = {4},
      number = {5},
      pages = {349-366},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1714304.pdf},
      abstract = {Advances in predictive analytics techniques have significantly transformed capital allocation strategies under volatile market conditions, enabling institutional investors, portfolio managers, and asset managers to enhance decision-making, optimize risk-adjusted returns, and improve resilience to market shocks. Predictive analytics leverages statistical models, machine learning algorithms, and big data methodologies to anticipate asset price movements, identify emerging risk exposures, and support scenario-based portfolio optimization. Traditional approaches to capital allocation, often reliant on historical performance and mean-variance frameworks, have proven inadequate in volatile or non-linear markets where correlations, volatility, and tail risks fluctuate dynamically. Predictive techniques address these limitations by incorporating time-series forecasting, regime-switching models, and real-time data analytics, allowing decision-makers to adjust allocations proactively and respond to rapid market changes. Recent innovations include hybrid models that combine traditional optimization frameworks with machine learning, Bayesian updating, and robust stochastic modeling. These methods enhance the estimation of expected returns, covariances, and downside risk metrics, mitigating estimation errors and model uncertainty. Additionally, predictive analytics facilitates multi-period and multi-asset portfolio optimization, enabling investors to balance short-term liquidity needs with long-term strategic objectives. Stress testing and scenario simulations, informed by predictive models, allow capital allocation strategies to account for extreme events, systemic shocks, and market contagion effects, thereby improving portfolio resilience. The integration of alternative data sources, including macroeconomic indicators, social sentiment, and ESG metrics, further strengthens predictive capacity, particularly in emerging or less liquid markets. Despite these advances, challenges remain in model interpretability, data quality, and governance, underscoring the importance of embedding predictive analytics within structured risk-based frameworks and oversight mechanisms. Predictive analytics techniques represent a pivotal advancement in capital allocation under volatile market conditions, enhancing risk management, investment performance, and strategic decision-making. By combining quantitative rigor with forward-looking insights, these techniques enable institutional investors to navigate uncertainty, optimize portfolio allocations, and maintain resilience in dynamic financial environments.},
      keywords = {Predictive Analytics, Capital Allocation, Portfolio Optimization, Risk Management, Volatility, Machine Learning, Bayesian Models, Emerging Markets, Stress Testing, ESG Integration.},
      month = {November},
  }